Xiaokang Qin

To address the challenges of synthetic aperture radar (SAR) ship detection in complex port environments, including strong background clutter, large-scale variations, and frequent missed detections of small vessels, this paper proposes a lightweight attention-guided multi-scale fusion detector, termed LAMFDet, based on YOLOv8. The proposed framework enhances the baseline architecture from three complementary perspectives: multi-scale feature enhancement, lightweight adaptive feature extraction, and efficient feature reconstruction. Specifically, a multi-scale feature enhancement module (SPPFSENetV2) integrating spatial pyramid pooling with channel attention is designed to strengthen scale-aware contextual representations. A lightweight adaptive extraction (LAE) module is introduced to improve small-target perception while maintaining computational efficiency. Furthermore, an efficient upsampling convolution block (EUCB) is incorporated to preserve structural details and enhance feature fusion quality across feature levels. Extensive experiments on two public SAR ship datasets, HRSID and SSDD, demonstrate the effectiveness and generalization capability of LAMFDet. On HRSID, LAMFDet achieves 94.79% Precision and 71.53% mAP@0.5:0.95, surpassing YOLOv8n by 2.81% and 14.92%, respectively. On SSDD, it further attains 99.65% Precision and 82.05% mAP@0.5:0.95, indicating strong robustness under diverse SAR imaging conditions. These results confirm that LAMFDet effectively improves detection completeness and localization accuracy for small and densely distributed vessels while maintaining favorable efficiency, highlighting its practical potential for real-time port monitoring and maritime surveillance.

In traffic scenes, pedestrian target detection faces significant issues of misdetection and omission due to factors such as crowd density and obstacle occlusion. To address these challenges and enhance detection accuracy, we propose an improved CCW-YOLO algorithm. The algorithm first introduces a lightweight convolutional layer using GhostConv and incorporates an enhanced C2f module to improve the network’s detection performance. Additionally, it integrates the Coordinate Attention module to better capture key points of the targets. Next, the bounding box loss function CIoU loss at the output of YOLOv5 is replaced with WiseIoU loss to enhance adaptability to various detection scenarios, thereby further improving accuracy. Finally, we develop a pedestrian count detection system using PyQt5 to enhance human–computer interaction. Experimental results on the INRIA public dataset showed that our algorithm achieved a detection accuracy of 98.4%, representing a 10.1% improvement over the original YOLOv5s algorithm. This advancement significantly enhances the detection of small objects in images and effectively addresses misdetection and omission issues in complex environments. These findings have important practical implications for ensuring traffic safety and optimizing traffic flow.