Computer vision, a branch of artificial intelligence, enables machines to understand and interpret visual information like images and videos from an AI-enabled camera. By harnessing computer vision and machine learning techniques, especially neural networks and deep learning, computer vision can transform a camera into a purpose-specific vision camera that can recognize optical characters, classify images, and identify objects.
Computer vision technology is significantly changing how cameras work and what they can be used for. Rather than relying on a human to monitor a video stream, the computer vision application can interpret the visual data in real time and generate responses based on what it detects in its field of view. Without human intervention, the AI application can replace manual monitoring efforts by extracting only the most important information, depending on the use case.
Recent research from O’Reilly reveals that most AI adopters are still in the early stages, with only 18% having deployed applications in production. In this white paper, we will explore computer vision in depth, outline the pain points that warehouse and logistics operators experience, and how to tackle these challenges with computer vision solutions to ensure they can streamline their processes and scale with success.
Computer vision forms the foundation of computer vision cameras. It allows cameras to interpret visual inputs like humans do. Through computer vision technology, cameras can recognize objects, track movements, detect anomalies, and even analyze behaviour patterns in real time.
Deep learning and neural networks have empowered the evolution of computer vision. These systems can process a vast amount of data to identify patterns, process visual data, and achieve accurate results in real-time.
Imagine that you have a camera in your home that can not only see whether the lights are on or off, or if the fan is running, but also lets you monitor and control everything from your phone or computer, no matter where you are. It’s like having a smart home assistant that helps you save energy and reduces the stress of forgetting to switch things off.
Nowadays, there are many dedicated sensors available that can be used to build such a system. But you can use a camera integrated with computer vision technology as an alternative solution because it can help you save money on hardware and installation, and can detect various appliances without needing specific sensors for each, making it flexible for different needs
According to research made by McKinsey & Company, 30% of end-to-end AI solutions are constrained by functional silos. Another note is that 91% of AI projects cannot meet clients’ expectations, either in terms of their benefits or the time invested.
The key point when designing a computer vision system is selecting the right cameras and computing hardware that can meet the demands of the use cases. It's essential to ensure that the images captured are appropriate and useful for addressing the specific problem at hand. Equally important is the seamless integration of the computer vision system with existing infrastructure or building, as well as any existing analytics and device management platform. Additionally, developing the right algorithms is crucial, particularly when the system is being implemented within warehouse management operations and workflow automation.
Developing robust algorithms for handling tasks like object recognition or defect detection is relatively straightforward. However, these systems provide significant value, especially in warehouses and logistics centers, due to their ability to monitor entire processes. Consider the process of loading and unloading boxes from a pallet. To capture a full and actionable picture of this operation, a computer vision system must combine several advanced capabilities: detecting individual products, tracking vehicle movements, analyzing space usage, and identifying human activity. By bringing these elements together, the system delivers holistic process visibility, enabling more informed decisions, streamlined workflows, and greater overall efficiency in warehouse and logistics environments.
2.1. Acquiring the Right Data
To ensure accurate results, the quality of the initial data is critical and should never be underestimated. Both the data and the subsequent AI models developed from it must accurately represent the real-world scenarios that the computer vision system will encounter. In some cases, a problem can be effectively modeled with just a few hundred well-chosen images, rather than thousands. This emphasizes the importance of providing high-quality data for the computer vision system to learn from. Ensuring data diversity is essential, with datasets covering all relevant use case variations. Equally important are considerations of data privacy, ethical use, and security to ensure responsible and secure AI implementation.
2.2. Adapting to scene and product changes
Flexibility is key when designing a computer vision system. The system must be capable of adapting to environmental changes, such as warehouse layouts or product lines, without the need for extensive reprogramming. This flexibility can be achieved by using tools that enable quick data capture (e.g., for new product packaging) or the generation of synthetic data, allowing for fast and efficient retraining of the AI model.
2.3. Validating the business case
Before implementing a computer vision system, it’s crucial to validate the business case to ensure the investment aligns with strategic goals and delivers measurable value. This process involves identifying the problems to be solved, defining key performance indicators (KPIs), and estimating the return on investment (ROI) for adopting computer vision technology.
Next, researching to assess the operational impact on current operations and the workforce will help you ensure that the computer vision system enhances operations rather than causing disruptions.
2.4. Scaling of Proof-of-concept
It is essential to ensure that the infrastructure can scale from proof of concept (POC) to full deployment, facilitating the wider adoption of AI technology. Businesses should balance the costs of scaling, including hardware, software, and maintenance, against the project's long-term benefits. It involves a strategic investment approach that can accommodate future growth without constant reinvestment. As systems expand, maintaining consistent performance and reliability becomes more challenging
To address these pain points, businesses have to prepare a complete end-to-end approach and collaborate with experienced partners who can provide helpful advice and support. To minimize costs for investing in an R&D team with a wide interdisciplinary range of expertise, businesses can partner with AI software experts and hardware manufacturers to help provide holistic solutions and speed up AI adoption.
As businesses increasingly adopt computer vision to enhance operational efficiency, understanding the computer vision workflow becomes essential for successful implementation and scaling. Sky Solution recommends some aspects that businesses should consider elaborately, including: corporate strategy, scenarios, data foundation, teams, partnerships, POC, and implementation
3.1. Aligning with Strategic Objectives
The first and most important step in adopting AI into business operations is ensuring that its workflow aligns with the organization’s strategic objectives. Whether the goal is to improve product quality, reduce operational costs, enhance safety, or optimize supply chains, computer vision must directly support measurable business outcomes.
By starting with a clear strategy and aligning the computer vision workflow accordingly, businesses can move confidently from pilot projects to full-scale implementation, unlocking the full potential of AI-powered visual automation.
3.2 Define Use Cases
To find the best opportunities for AI implementation, start by identifying tasks where technology can surpass human performance is the correct strategic method. A detailed assessment of workflows helps highlight where AI adds the most value.
3.3. Build data foundation
AI systems are used for tasks like prediction, classification, or automation, so they depend entirely on high-quality, relevant, and well-structured data to function effectively. The robustness of the company’s data infrastructure and the depth of its data comprehension are critical factors in determining the success of its AI initiatives. A strong data foundation ensures that the AI models are trained on accurate and representative datasets, enabling them to generate reliable insights and decisions.
3.4. Establish Teams and Partnerships
To effectively develop AI capabilities, warehouse and logistics center operators must build teams with deep expertise in AI, strong domain knowledge, and practical experience in applying AI to real-world operations. These internal teams are essential for aligning AI initiatives with business objectives and ensuring seamless implementation. Additionally, strategic partnerships with technology providers, AI specialists, or system integrators can accelerate the AI journey by bringing in external expertise, access to advanced solutions, and critical support for scaling initiatives efficiently and sustainably.
3.5. Run POC and Implement at Scale
Once a well-defined use case, solid data foundation, and a skilled team are in place, the next step in implementing AI in operations is to run a Proof of Concept (POC) and scale successful solutions across the organization. The POC phase allows businesses to test AI models in a controlled environment, validate their effectiveness, and identify potential challenges before full deployment. Following the successful demonstration of the prototype, organizations can proceed to scale the solution across relevant functions or sites. This stage requires robust change management, cross-functional collaboration, and continuous monitoring to ensure the AI system delivers consistent performance and adapts to evolving business needs.
4.1. Warehouse management
In warehouse operations, computer vision is applied to a wide range of tasks, including packaging quality inspection, warehouse surveillance, QR code scanning, product counting, PPE compliance monitoring, asset tracking, and more. Among the most common use cases are loading dock monitoring and inventory management. Traditional inventory processes often rely on manual data entry, which can be time-consuming and prone to errors, slowing down overall efficiency. Computer vision technology helps automate these processes, ensuring real-time accuracy. At the loading dock, AI-powered monitoring provides traceable loading activities, improves container space utilization, and offers visual evidence to address potential damage claims, enhancing both accountability and operational performance.
PPE Monitoring
Vehicle tracking
4.2. Employee PPE detection and safety
PPE and a variety of other safety gear are at the core of safety in the workplace. Computer vision is utilized to detect Personal Protective Equipment (PPE) by automating the identification of whether workers are wearing mandatory safety gear, such as helmets, gloves, or high-visibility vests. This is enabled by object detection models trained to recognize specific PPE items within images or video footage. For example, a construction site might deploy cameras connected to a computer vision system that scans workers in real time, automatically flagging individuals who are not wearing a hard hat.
A practical implementation could involve training a computer vision model on annotated datasets featuring workers wearing PPE across different environments. For example, the model can learn to differentiate between various types of protective gear, such as safety goggles and regular glasses, by analyzing features like shape, color, and contextual cues (e.g., goggles typically used in laboratory settings). This enables the system to make accurate distinctions and enhance safety compliance monitoring.
An example use case is a construction site where cameras equipped with computer vision monitor entry points, detecting whether workers are wearing helmets and high-visibility vests. If a worker attempts to enter without the required PPE, the system automatically denies access by locking the gate and sending an alert to the site supervisor. This integration with the access control system ensures only properly equipped personnel are allowed into hazardous zones, reducing the risk of injury and improving overall site compliance.
In case a violation is detected, the system immediately notifies safety engineers to take action. In the event of an active accident, the computer vision system can alert managers and staff with precise information about the location and severity of the incident, allowing operations to be paused in the affected area and enabling swift, proactive measures to protect employee safety.
4.3. Product identification
Computer vision plays a crucial role in product identification by enabling fast, accurate, and automated recognition of goods throughout warehouse operations. Using high-resolution cameras and AI-driven image processing, the system can detect and identify products based on visual features such as shape, size, labels, logos, barcodes, or QR codes. This becomes crucial during time-sensitive operations like truck or container loading, where confirming item quantities and characteristics is necessary to proceed efficiently to the next phase.
In addition, product identification using computer vision is a powerful technology to bring the best of online shopping into the supermarkets across the world. By analyzing images or video feeds, computer vision systems can automatically detect and recognize items such as consumer goods, retail products, or industrial components. This is achieved through advanced algorithms that extract meaningful features from visual data and compare them with a pre-trained database or utilize machine learning models to deliver accurate predictions.
Product identification
4.4. Detecting empty shelf
Product availability is a key driver of both revenue and customer satisfaction in retail. When shelves are fully stocked, customers can easily find the items they need, resulting in steady sales, stronger brand loyalty, and a positive shopping experience. In contrast, empty shelves and missing products can lead to lost sales, reduced foot traffic, and diminished customer trust.
To stay competitive, modern retailers must prioritize effective inventory management, and a critical part of this is accurately detecting empty shelf space. Traditional manual checks are often time-consuming, labor-intensive, and prone to human error. The emergence of computer vision and deep learning presents an opportunity to revolutionize this practice.
Detecting empty shelves has become one of the most impactful applications of computer vision in retail. By leveraging AI-powered image analysis, retailers can automate shelf monitoring, receive real-time stock alerts, and make faster, data-driven decisions. This not only improves operational efficiency but also ensures customers consistently find the products they’re looking for, enhancing satisfaction and loyalty.
4.5. Truck loading and monitoring
Detecting vehicles at a loading dock presents several challenges, primarily due to environmental and operational complexities. First, varying lighting conditions, such as glare from sunlight, low visibility at night, or shadows from nearby structures, can significantly impact the accuracy of vision-based detection systems. These conditions make it difficult for cameras to consistently capture clear images of vehicles. Second, the diversity in vehicle types and unpredictable positioning adds to the challenge. Trucks and delivery vehicles come in different shapes, sizes, and colors, and they may not always align perfectly with designated docking zones. Some may approach at unusual angles or park too far forward or backward, requiring detection systems to be adaptable and precise in real-time. Last but not least, there’s the challenge of tracking vehicles throughout the entire facility, across multiple operations, from entry and exit points to loading docks, weighing stations, and beyond.
Computer vision systems help mitigate all these challenges in a unified, scalable and cost-efficient way. By integrating license plate recognition, barcode ledger scanning, pallet QR scanning, and real-time image logging, computer vision can enhance efficiency and accuracy while eliminating the manual effort involved in traditional loading documentation. It continuously detects vehicle presence, identifies available bays, and verifies correct truck alignment before dock doors open, preventing errors and accidents. The system also sends real-time alerts to operations management, improving efficiency while ensuring safety and security throughout the facility.
5.1. About our system
Sky Solution’s Face Recognition system harnesses cutting-edge AI technology to deliver highly accurate face and marker identification. Powered by advanced AI models and accessible through user-friendly APIs, the platform streamlines development processes while significantly enhancing operational efficiency.
The system offers a comprehensive suite of video analytics capabilities, including real-time analytics, facial recognition, and intruder detection, achieving up to 99% recognition accuracy with rapid inference speeds. Compatible with both Android and Linux environments, it supports a broad range of biometric applications, making it an ideal solution for enterprises, retail, hospitality, and public space deployments.
Our system offers exceptional management capacity while keeping computational costs low. It can efficiently handle up to 32 cameras with 4K resolution simultaneously on a single workstation equipped with just one NVIDIA A4000 graphics card, costing under $1,000. The solution supports virtually any camera that uses the ONVIF protocol, which is standard among most commercial CCTV cameras today, ensuring broad compatibility. With an on-premise deployment model, all data remains securely within your control rather than relying on third-party cloud storage, making it a cost-effective choice in the long run. Additionally, the modular architecture allows rapid scalability; simply add more GPU cards or workstations to expand camera support as your needs grow.
5.2. Usage Scenario
In enterprise: Sky Solution’s Face Recognition system enhances enterprise security and operational efficiency by enabling seamless door access control and precise attendance management. Additionally, it helps optimize meeting room utilization through effective capacity management, ensuring smooth daily operations in corporate environments.
In retail: In retail settings, our system supports essential safety and security measures such as mask detection and blacklist checking to prevent entry of unwanted individuals. These features contribute to a safer shopping experience for both customers and staff.
In hospitality: For the hospitality industry, Sky Solution’s platform offers robust membership management and contactless check-in/out capabilities, ensuring convenience and safety for guests. The system also incorporates mask detection to maintain health standards, enhancing the overall guest experience.
In Factory & Warehouse: In factory and warehouse environments, the system strengthens security with reliable door access control and blacklist verification to restrict unauthorized personnel. It also provides stranger warning alerts, enabling quick responses to potential security threats and protecting valuable assets.
5.3. Product advantage
Sky Solution’s Face Recognition technology offers key product advantages that enhance both accuracy and user experience. With Quick Photo Validation, the system swiftly verifies identities while detecting mismatches such as covered faces or outdated images. The Photo Scoring System provides a confidence percentage for each match, ensuring only high-quality image data is approved, thereby increasing reliability. Additionally, the solution features ID Classification, automatically distinguishing between employees and guests, streamlining access control and security operations within any facility.
The adoption of Artificial Intelligence (AI) in business has become a key driver of innovation, efficiency, and competitive advantage. With the rise of Industry 4.0, businesses are turning to computer vision to bring intelligence directly to visual data, transforming static camera feeds into actionable insights. This shift is being driven by advancements in edge computing, affordable hardware, and more accessible AI development platforms, making computer vision solutions more scalable and cost-effective than ever.
Companies across industries are embracing computer vision not only to automate repetitive tasks but also to improve accuracy, reduce reliance on manual labor, and respond more quickly to changing conditions. From real-time monitoring and safety enforcement to asset tracking and workflow optimization, computer vision is being adopted as a core component of digital transformation strategies.
Computer vision’s applications are rapidly expanding across various industries, delivering practical, real-world benefits. In logistics, computer vision powers smart cameras for truck loading and monitoring, ensuring accurate cargo placement, real-time tracking, and safety compliance. In the retail sector, it is used to detect empty shelves, helping businesses respond quickly to out-of-stock items and optimize restocking processes. In manufacturing, computer vision enables quality control and predictive maintenance by identifying defects and equipment wear before they cause downtime.
Adoption is no longer limited to large enterprises. Small and medium-sized businesses are also leveraging computer vision technologies to remain competitive, enhance agility, and gain new levels of visibility into their operations. Investing in computer vision is not just a technological upgrade, but a strategic tool for gaining a competitive edge in a data-driven economy.
