基于深度卷积的建筑施工现场多目标危险行为识别方法OA

A multi-objective method for recognizing dangerous behaviors based on deep convolution is proposed to address errors in manually monitoring and identifying such behaviors on construction sites.The on-site multi-target dangerous behavior recognition problem is decomposed into two steps:on-site monitoring image encoding,and multi-target dangerous behavior decoding and recognition.During the encoding stage,dense convolution and cross-layer fusion techniques are employed to extract and combine multi-target behavioral features from construction site monitoring images.These features are then decoded,after which a Softmax classifier is used to determine the probability that the decoded behavioral features belong to a given behavior type,thus achieving multi-target dangerous behavior recognition.Experimental results demonstrate that the proposed method can accurately identify the subtle behavioral differences of multiple targets on construction sites.