Improved YOLOv5-based Infrared Dim-small Target Detection under Complex Background

Using the traditional algorithm to meet the detection requirements of interference factors, such as complex background and noise, relying on the precise separation and information extraction of infrared targets and environmental background, is difficult. This paper presents a dim–small target detection method for an infrared imaging algorithm based on the improved YOLOv5 for complex backgrounds. Based on YOLOv5, an attention mechanism is introduced in the algorithm to improve the feature extraction ability and detection efficiency. In addition, the loss function and prediction box screening method of the original YOLOv5 target detection network are used to improve the accuracy of the algorithm for infrared dim–small target detection. In the experiment, seven sets of infrared dim–small target image datasets with different complex backgrounds are selected, the data are labeled and trained, and an infrared dim–small target detection model is established. Finally, the accuracy of the algorithm and model is evaluated in terms of the model training and target detection results. The experimental results show that the model trained by employing the improved YOLOv5 algorithm in this study has a significant improvement in detection accuracy and speed compared with several target detection algorithms used in the experiment, and the average accuracy can reach more than 99.6%. The model can effectively detect infrared dim–small targets in different complex backgrounds, and the leakage and false alarm rates are low.