基于改进YOLOv5s的建筑护栏目标检测OA北大核心CSTPCD

There are many incidents of construction workers falling from heights on construction sites due to the reduced safety of building guardrails.On this basis,a building guardrail detection algorithm based on improved YOLOv5s is proposed.In allusion to the safety hazards that are prevalent in building guardrails,images that have an impact on guardrail safety,such as images of the presence of building guardrails,images of missing building guardrails,images of guardrail nets,images of misaligned guardrail panel connections,and images of correct guardrail panel connections are collected,and these images are created for a training dataset.In order to improve the accuracy of YOLOv5s detection results when performing multi-target detection and discrimination tasks in complex environments,a novel Biformer attention mechanism combined with SE attention mechanism is embedded into the feature extraction network of the original model,and CBAMC3 is used to replace the C3 module of the original feature extraction network.The use of CLAHE algorithm can largely solve the problem of dim brightness in some images,which affects detection accuracy.The experimental results show that the mAP50 value and recall of the proposed detection algorithm can reach 79.6%and 83%,respectively,which are 3.7%and 6.8%higher than those of the original YOLOv5s algorithm.