In modern life, traffic congestion is widespread in large and medium-sized cities in various countries. Multi-scale vehicle targets are densely distributed and occluded from each other in the images of crowded scenes. Vehicle detection in such scenarios is of great significance to urban traffic control, safety management and criminal investigation, but also has great challenges. Facing the special application in congested traffic, we propose Simplified Weighted Dual-path Network with Guided Anchoring framework to realize real-time vehicle detection. Firstly, a simplified weighted Dual-path Feature Pyramid Network (SWD-FPN) is used to improve the robustness of the model for multi-scale and partially occluded objects. Secondly, in order to improve the detection capability for vehicles with wide range of scale changes, the Guided Anchoring (GA) is applied to generate anchors of corresponding positions and scales according to the feature maps. Finally, for the challenge of vehicles intensive distribution, DIoU-soft NMS post-processing mechanism is introduced to reduce the missing alarm. Considering the class imbalance of vehicle detection in real-time traffic scenarios and the above improvements, multi-task loss is proposed for training. Ablation experiments are performed on UA-DETRAC dataset to further analyze the effect of different strategies on performance improvement. In addition, comparisons experiments on UA-DETRAC dataset and handcrafted Vehicles of Traffic (VOF) dataset are conducted to demonstrate the superiority of the proposed method over other state-of-the-art methods.
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