The swift and precise detection of vehicles holds significant research significance in intelligent transportation systems (ITS). However, current vehicle detection algorithms encounter challenges such as high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm for YOLOv7-tiny called Ghost-YOLOv7. The model first scales the width multiple to 0.5 and replaces the standard convolution of the backbone network with Ghost convolution to achieve a lighter network and improve the detection speed; secondly, a Ghost bi-directional feature pyramid network (Ghost-BiFPN) neck network is designed to enhance feature extraction capability of the algorithm and enrich semantic information; thirdly, a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and class, enhancing model accuracy; finally, a coordinate attention mechanism is introduced in the output layer to suppress environmental interference, and the WIoU loss function is employed to enhance the detection accuracy further. Experimental results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model, achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, and 1.1% higher mean average precision (mAP), while achieving a detection speed of 428 FPS. These results validate the effectiveness of the proposed method.

翻译：在智能交通系统中，快速精确地检测车辆具有重要的研究意义。然而，当前车辆检测算法面临计算复杂度高、检测率低以及移动设备部署可行性有限等挑战。为解决上述问题，本文提出一种名为Ghost-YOLOv7的轻量级YOLOv7-tiny车辆检测算法。该模型首先将宽度倍数缩放至0.5，并使用Ghost卷积替代主干网络的标准卷积，以实现更轻量化的网络结构并提升检测速度；其次，设计Ghost双向特征金字塔网络（Ghost-BiFPN）颈部网络，以增强算法的特征提取能力并丰富语义信息；再次，采用Ghost解耦头（GDH）实现车辆位置和类别的精确预测，提高模型精度；最后，在输出层引入坐标注意力机制以抑制环境干扰，并采用WIoU损失函数进一步提升检测精度。在PASCAL VOC数据集上的实验结果表明，Ghost-YOLOv7优于原始YOLOv7-tiny模型，计算量降低29.8%、参数量减少37.3%、模型权重减少35.1%、平均精度均值（mAP）提升1.1%，同时检测速度达到428 FPS。这些结果验证了所提方法的有效性。