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Abstract：
Vehicle detection is a research direction in the field of target detection and is widely used in intelligent transportation, automatic driving, urban planning, and other fields. To balance the high-speed advantage of lightweight networks and the high-precision advantage of multiscale networks, a vehicle detection algorithm based on a lightweight backbone network and a multiscale neck network is proposed. The mobile NetV3 lightweight network based on deep separable convolution is used as the backbone network to improve the speed of vehicle detection. The icbam attention mechanism module is used to strengthen the processing of the vehicle feature information detected by the backbone network to enrich the input information of the neck network. The bifpn and icbam attention mechanism modules are integrated into the neck network to improve the detection accuracy of vehicles of different sizes and categories. A vehicle detection experiment on the Ua-Detrac dataset verifies that the proposed algorithm can effectively balance vehicle detection accuracy and speed. The detection accuracy is 71.19%, the number of parameters is 3.8 MB, and the detection speed is 120.02 fps, which meets the actual requirements of the parameter quantity, detection speed, and accuracy of the vehicle detection algorithm embedded in the mobile device.
摘要：
车辆检测是目标检测领域的一个研究方向,在智能交通中有着广泛的应用,自动驾驶,城市规划,和其他领域。为了平衡轻量级网络的高速优势和多尺度网络的高精度优势，提出了一种基于轻量级骨干网络和多尺度颈部网络的车辆检测算法。采用基于深度可分离卷积的移动NetV3轻量级网络作为骨干网络,提高车辆检测速度。icbam注意机制模块,用于加强对骨干网络检测到的车辆特征信息的处理,以丰富颈部网络的输入信息。将bffpn和icbam注意力机制模块集成到颈部网络中，以提高不同尺寸和类别车辆的检测精度。在Ua-Debrac数据集上的车辆检测实验验证了该算法能够有效地平衡车辆检测精度和速度。检测准确率为71.19%,参数的数量是3.8MB，检测速度为120.02fps，满足参数数量的实际要求，检测速度,以及嵌入在移动设备中的车辆检测算法的准确性。
