Small-Target Detection Based on Improved YOLOv8 for Infrared Imagery

Infrared small-target detection plays a crucial role in applications such as public safety monitoring. However, it faces significant challenges due to the loss of target features, which weakens detection performance. To tackle this problem, this study proposes an improved infrared small-target detection model based on YOLOv8n. First, the Dual-Path Fusion Downsampling Convolution (WFDC) module enhances the backbone network’s ability to extract fine-grained features of targets, preventing the loss of image details as the depth of the convolutional neural network increases. Second, the Involution and the Coordinate Attention (CA) mechanisms are integrated into the spatial pyramid pooling module, where self-attention and the Involution modules aggregate contextual semantic information over a broader spatial range, enriching channel information at each scale. Finally, deformable convolutions are incorporated into the backbone of the model, enabling better handling of target deformations across various scenarios. Experiments conducted on the SIRST-5K and IRSTD-1K datasets demonstrate that our method outperforms both the baseline YOLOv8 model and several state-of-the-art YOLOv8-based improved detection methods. The results show that, compared to the baseline model, our approach achieves mAP@[0.5:0.95] improvements of 12.3% and 16.4% on the two datasets, respectively. These results highlight the effectiveness of our proposed enhancements in improving detection accuracy and model robustness.