Abstract: In response to the challenges of inaccurate detection and low recognition rates in human target detection under low-quality imaging with laser through-window technology,an enhanced target detection algorithm,YOLO-TC,based on YOLOv8n optimization,has been proposed.The feature extraction module of the backbone has been redesigned to enhance the model’s multi-scale feature representation capability.Pruning of the YOLOv8n model has been employed to optimize the network structure,reduce model complexity,and enhance detection accuracy.An EMA attention mechanism module has been introduced between the C2f module and the detection head(Detect) to improve semantic and location information in feature fusion and enhance the model’s feature fusion ability.Using SIoU bounding box regression loss function instead of the original loss function to improve the inference accuracy and training speed of the algorithm.Experimental results on a laser through-window imaging dataset demonstrate that the Precision,Recall,and mean Average Precision(mAP) of the improved model has increased by 7.7%,5.9%,and 7.0% respectively.Furthermore,the model size has been reduced by 34.6% compared to the original model,making it suitable for subsequent edge hardware deployment.

Key words: Laser through-window imaging, YOLOv8, Multi-scale feature extraction, Model pruning, Attention mechanism, SIoU