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：快速、精确的车辆检测在智能交通系统（ITS）中具有重要的研究意义。然而，当前的车辆检测算法面临计算复杂度高、检测率低以及移动设备可行性有限等挑战。针对这些问题，本文提出了一种基于YOLOv7-tiny的轻量级车辆检测算法，称为Ghost-YOLOv7。该模型首先将宽度倍数缩放到0.5，并用Ghost卷积替换骨干网络的标准卷积，以实现更轻量的网络并提升检测速度；其次，设计了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；第三，采用Ghost解耦头（GDH）精确预测车辆位置和类别，提升模型准确率；最后，在输出层引入坐标注意力机制抑制环境干扰，并采用WIoU损失函数进一步提高检测精度。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%，模型权重减小35.1%，平均精度均值（mAP）提高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。