Research on the small object detection algorithm based on deep-learning and visual attention mechanism

In the target detection task, this paper proposes a small object detection algorithm, which addresses the problems of weak feature information, complex background and difficult identification, combining deep-learning and attention mechanism. The algorithm is based on the target detection convolutional neural network, which integrates the visual attention mechanism and focuses on the key feature areas, so as to improve the detection ability of small targets. In addition, the backbone network introduces CSP technology to extract features and enhance the feature representation capability, and the middle layer enhances features for small targets and weak targets to improve the detection of small targets. Through the optimization and innovation of the neural network, especially after the addition of the visual attention mechanism, the detection performance is significantly improved. The experimental results show that the model can effectively identify small targets, significantly improve the detection accuracy, and achieve good results in practical applications.