Small target detection algorithm based on improved YOLOv5

At present, the Internet and smart cities are developing rapidly, and all social services. Small target detection is widely used in these fields. This paper focuses on small target detection based on YOLOv5. The feature enhancement module is proposed to solve the problem of incomplete extraction of small target features by the model. An attention mechanism is added to the model, so that the detection model pays more attention to the region in the image that contains the target to be detected, increases the weight of the feature information in this part, enriches the feature space, and further improves the performance of small target detection. Finally, by combining the use of network pruning and knowledge distillation, the model is compressed to compress the model size and improve the detection speed under the premise of ensuring that the model detection accuracy is not affected. The experimental results show that our optimisation effectively improves small target detection in terms of both detection accuracy and detection speed, with FPS improved by 31.84 and AP improved by 1.7%.