复杂交通场景基于边缘特征增强的长队列排队检测

To address issues such as false detections, missed detections, and misjudgments of queue status in long-queue detection under surveillance views—caused by occlusions from dense traffic, aggregation of small distant targets, and inconsistent spatial scales between near and far regions in surveillance scenes—this paper proposes an edge-feature-enhanced long-queue detection method for complex traffic scenarios. In the object detection module, a novel Edge Feature Enhancement Pyramid (EFE-Pyramid) is designed, which incorporates an Edge Feature Convolution (EFConv) to explicitly extract shallow-layer edge response features, and employs a deep-shallow feature fusion mechanism to enhance edge features, thereby improving the model's discriminability and detection accuracy for occluded objects in dense traffic and small distant targets. On this basis, a task-aligned detection head is used to ensure real-time performance. For queue length estimation, an adaptive perspective-aware method is proposed to achieve unified alignment of target representations across near and far regions, enhancing the stability and generalization capability of queue length estimation. Experimental results demonstrate that, on both public datasets and a self-collected complex-traffic-scenario dataset, the proposed algorithm achieves higher recognition performance in complex traffic environments with relatively low model complexity, improving detection accuracy by 2.4 and 4.8 percentage points over the baseline model, respectively, and outperforming current state-of-the-art methods. Furthermore, in queue length estimation experiments, it achieves an average absolute percentage error of 4.8%, fully validating the effectiveness and practicality of the proposed approach for long-queue detection in complex traffic scenarios.