YOLOv7 Optimization Model Based on Attention Mechanism Applied in Dense Scenes

Abstract

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection.