PSF3-YOLOv8:Low resolution vehicle detection algorithm based on improved YOLOv8

Detecting vehicles at low resolution is essential for traffic surveillance and autonomous driving. Traditional high-resolution detection methods often struggle with low-resolution images, but super-resolution techniques can recover fine details, thereby significantly improving detection performance. In this paper, a target detection method that is based on YOLOv8s is presented, utilizing the ADown convolution module in place of the Conv module to reduce parameters and improve efficiency. Additionally, the PSFM deep semantic fusion module is incorporated into the detection head to enhance feature fusion, while the Dattention mechanism is integrated into the C2f module to capture complex details. Experimental results demonstrate that the PSF3-YOLOv8 model outperforms other YOLO networks in key metrics, achieving improvements of 5.5% in Precision, 0.1% in Recall, 0.9% in mAP50, and 3.5% in mAP50-95.