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In the process of industrial construction, worker safety has become an increasingly important issue. Wearing safety equipment such as safety rope is an important measure to protect workers" life safety when working at height. In the process of modern production and construction, it is more and more common to use surveillance camera equipment combined with artificial intelligence algorithm to detect workers wearing safety ropes and other equipment, but safety ropes are difficult 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 using YOLOv5 is proposed. Firstly, context information is extracted by the improved FasterNet module, and more feature information is preserved by the improved multidimensional dynamic convolution in the Neck network. The WIoU_Loss loss function is used to improve the positioning accuracy, and the strategy of dynamically adjusting the learning rate is used in the training process. Experimental results show that the improved algorithm improves the detection accuracy by 3.0% and mAP@0.5 by 4.3% under the condition of reducing the computational complexity. After application in actual scenarios, the proposed algorithm can meet the requirements of the project on real-time detection accuracy and speed.