Infrared Image Target Detection Model Based on Separable Wide Attention and Hierarchical Feature Fusion Mechanism

In practical applications, there are a large number of small targets in infrared imaging data, and the existing detection model parameters are too large, making it difficult to achieve efficient detection of infrared imaging targets based on edge devices. In response to this issue, this paper proposes an infrared image object detection model based on Separable Wide Attention and Hierarchical Feature Fusion Mechanism. The proposed model embeds a Separable Wide Attention (SWA) module into a Small Detection Head (SDH) network to improve the recognition accuracy of small targets. In addition, the Hierarchical Feature Fusion Mechanism (HAF) module is integrated into the Neck. This design divides the input feature maps into multiple feature slices along the channel dimension and feeds them into different attention heads, thereby reducing the number of model parameters and ultimately enhancing the real-time performance of detection. The experimental results based on the extended M3FD dataset show that compared with existing mainstream models, the proposed model achieves an APsmall of 48.0, which is 10.1% higher than PPYOLOE+- m, and an FPS of 54.6, thus achieving efficient infrared target detection.