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Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation. [ABSTRACT FROM AUTHOR]

Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation.