Abstract: An improved model based on YOLOv5s is proposed for the problem of target miss detection in remote sensing images with complex targets in the background and blurred imaging due to small vehicles. A new backbone network structure is designed for the improved model: RepVGG network is selected for the backbone feature extraction of the improved model， while an attention mechanism， CoordAttention， is added to the backbone network to improve the perception capability of the model for small targets. Multi-scale feature fusion is added to improve the detection accuracy of the improved model for small targets， and the loss function of border regression is chosen to use DIoU to help the improved model achieve more accurate localization. After experiments， it is demonstrated that the improved YOLOv5 model improves the detection accuracy by 5.3 percentage points for target detection in remote sensing images compared to the original model in small target vehicles， and improves the mAP by 16.88 percentage points compared to Faster R-CNN. The improved model can have a larger detection accuracy improvement compared with the mainstream detection algorithms， and has a better detection accuracy than the original YOLOv5s model for small vehicle detection in remote sensing images.

Key words: remote sensing image recognition, target recognition, YOLO, attention mechanisms, multi-scale feature fusion