Method for detecting reflective vests and safety helmets in complex operational environments
In response to the limitations of existing reflective vest and safety helmet detection algorithms in complex site environments,such as low detection efficiency,poor accuracy,and difficulty in effectively distinguishing small differences between the target and the background,this paper proposes an enhanced algorithm based on YOLOX.Firstly,the spatial pyramid pooling in the backbone network now utilizes average pooling instead of maximum pooling.This adjustment aims to eliminate the influence of local maxima,reduce information loss,and mitigate the risk of overfitting in the feature map.Secondly,a Weighted Convolutional Block Attention Module(W-CBAM)has been developed and integrated into the feature fusion layer.This module enhances spatial dimension expression in the feature map by leveraging weight coefficients,emphasizing target region features,and guiding the network to focus more on the target being detected to enhance detection accuracy.Finally,an Adaptively Spatial Feature Fusion(ASFF)module has been incorporated to dynamically merge feature maps of varying scales.This addition effectively captures target feature information across different scales,boosting the model's ability to perceive and represent the target accurately.The study conducted experiments on an augmented public dataset for reflective vests and safety helmets,incorporating data enhancement techniques like image flipping and noise addition.The outcomes reveal that the enhanced algorithm achieves a mean average precision of 98.79％,with precision and recall rates of 98.72％and 94.63％respectively.The algorithm substantially reduces misdetections and misjudgments,outperforming not only the original YOLOX algorithm but also other state-of-the-art algorithms.Simultaneously,the algorithm achieves a detection speed of 68.47 frames per second,enabling real-time detection with high accuracy.The method presented in this study effectively addresses the issue of information loss during maximum pooling in the feature map,enhancing feature map expression and demonstrating precise and efficient performance on high-quality datasets with abundant samples.It adequately fulfills the detection requirements in construction environments and exhibits promising application potential.
safety engineeringreflective vest detectionsafety helmet detectionYOLOXattention moduleadaptive spatial feature fusion
万方数据
维普
