A Survey of Computer Vision Applications in Industrial Environments

In recent years, Computer Vision (CV) has evolved rapidly, enabling enhanced automation, safety, inspection, and logistics across manufacturing, construction, and robotics. This systematic review surveys current computer vision applications in industrial environments, spanning factory defect inspection, construction-site safety, warehouse inventory tracking, and industrial robotics. From more than 8,700 initially identified studies, we selected 146 key papers representing cross-domain CV applications. The survey is organized into four thematic areas: visual inspection and defect detection, worker safety and monitoring, inventory and logistics tracking, and robotics and automation. For each area, we compare state-of-the-art approaches, datasets, and deployment settings while highlighting the factors that influence their performance under different operating conditions. For example, YOLO-based detectors dominate real-time edge deployment because of their high throughput, whereas transformer-based models often struggle to scale in resource-constrained environments despite their superior feature extraction capabilities. We also identify critical research gaps. Although manufacturing and construction benefit from numerous public datasets for quality inspection and safety monitoring, warehousing and robotics continue to lack large-scale, real-world benchmark datasets. Finally, we discuss emerging research directions, including domain adaptation, edge AI, and explainable artificial intelligence (XAI), to support the development of more robust, scalable, and balanced computer vision solutions for industrial applications.