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:智能交通系统(ITS)中,快速准确检测车辆对于智能交通具有重要研究意义。然而,目前的车辆检测算法面临着高计算复杂度、低检测率、且不能在移动设备上使用等挑战。为了解决这些问题,本文提出了一种基于轻量级YOLOv7-tiny的车辆检测算法Ghost-YOLOv7。该模型首先将宽度多路复用比例缩小到0.5,用Ghost卷积替换主干网络标准卷积,实现轻量级网络和提高检测速度;其次设计了Ghost双向特征金字塔网络(Ghost-BiFPN)颈部网络,增强算法的特征提取能力和丰富语义信息;第三,采用Ghost解耦头(GDH)进行车辆位置和类别的精确预测,提高模型准确性;最后,在输出层引入坐标注意机制,抑制环境干扰,采用WIoU损失函数进一步提高检测准确率。在PASCAL VOC数据集上的实验结果表明,Ghost-YOLOv7模型优于原始YOLOv7-tiny模型,计算量减少29.8%,参数数目减少37.3%,模型重量减少35.1%,而平均精度(mAP)提高1.1%,同时达到428 FPS的检测速度。这些结果验证了提出方法的有效性。