Liu Yuhang, Zhou Huibo, Zhao Ming
School of Mathematical Sciences, Harbin Normal University, Harbin, Heilongjiang Province, 150500, China.
PLoS One. 2025 May 19;20(5):e0323410. doi: 10.1371/journal.pone.0323410. eCollection 2025.
Target detection is an essential direction in artificial intelligence development, and it is a crucial step in realizing environmental awareness for intelligent vehicles and advanced driver assistance systems. However, the current target detection algorithms applied to complex real-life scenarios still have a lot of intractable problems, such as the detection of different road scenarios, not having a good real-time detection capability, and so on. Therefore, there is a need to balance the efficiency and effectiveness of the target detector. YOLOv7, as a single-stage target detection algorithm, combines a number of advanced modules and methods for the purpose of achieving more precise and faster target detection. This paper, YOLOv7 is used as a baseline, combined with deformable convolution, to realize adequate detection in complex scenes and balanced computational efficiency and accuracy by adding an attention mechanism module. In addition, combining the lightweight network module speeds up the model's computational speed while improving the detector's feature expression ability, thus accomplishing the task of real-time target detection in complex traffic scenes. Compared with YOLOv7, our model improves the average accuracy by 3.7% on the SODA 10M dataset, and the mean average precision (mAP) value reaches 75.9%.
目标检测是人工智能发展的一个重要方向，也是实现智能车辆和高级驾驶员辅助系统环境感知的关键一步。然而，当前应用于复杂现实生活场景的目标检测算法仍然存在许多棘手的问题，比如不同道路场景的检测、缺乏良好的实时检测能力等等。因此，需要平衡目标检测器的效率和有效性。YOLOv7作为一种单阶段目标检测算法，结合了许多先进的模块和方法，旨在实现更精确、更快的目标检测。本文以YOLOv7为基线，结合可变形卷积，通过添加注意力机制模块在复杂场景中实现充分检测，并平衡计算效率和准确性。此外，结合轻量级网络模块在提高检测器特征表达能力的同时加快了模型的计算速度，从而完成复杂交通场景下的实时目标检测任务。与YOLOv7相比，我们的模型在SODA 10M数据集上平均准确率提高了3.7%，平均精度均值（mAP）值达到75.9%。
