Safe Rope Target Detection Based on Improved YOLOv5
In the process of industrial construction,the safety of workers has become an increasingly important issue,wearing safety equipment such as safety rope is an important guarantee for the safety of workers during high-altitude operations.In the process of modern production and construction,surveillance camera equipment are widely used to detect workers wearing safety ropes and other equipment combined with artificial intelligence algorithm,but it is difficult for the safety rope to accurately identify due to factors such as slender,changeable shape and environmental changes.In order to solve the above problems and ensure that the safety rope can be accurately identified in different environments,an object detection algorithm based on YOLOv5 is proposed.Firstly,the improved FasterNet module is used to extract the context information,and the improved multidimensional dynamic convolution is used to preserve the more feature information in the Neck network.The WIoU_Loss loss function is used to improve the positioning accu-racy,and dynamically adjust the learning rate in the training process.Experimental results show that under reducing the computation-al complexity,the improved algorithm improves the detection accuracy by 3.0％,and mAP@0.5 by 4.3％.By the application in ac-tual scenarios,the proposed algorithm can meet the requirements of real-time detection accuracy and speed in the project.
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