During automatic driving, the complex background and mutual occlusion between multiple targets hinder the correct judgment of the detector and miss detection. When a close-range target is captured again, the vehicle may not be able to respond in time and cause a fatal accident. Therefore, in the application of auxiliary systems, a model that can accurately identify partially occluded targets in complex backgrounds and perform short-term tracking and early warning of completely occluded objects is required. This paper proposes a method to improve detection accuracy while supporting real-time operations based on YOLOv3 and realize real-time warnings for those objects that are completely blocked. First, we obtain a more suitable prior frames setting through class-wise K-means clustering. To solve the problem that the maxpool operation of original CBAM easily introduces background noise, we proposed AS-CBAM(Adaptive Selection Convolutional Block Attention Module) and innovatively combined the HDC(Hybrid Dilated Convolution) to maximize the receptive field and fine-tune the characteristics. The 1×1 convolution operation is used to suppress the increase of the parameter amount. In this study, DIOU-NMS was used to replace traditional NMS. Besides, a tracking algorithm based on Kalman filtering and Hungarian matching is introduced to improve the system’s ability to recognize occluded objects. Compared with the traditional YOLOv3, the proposed method can increase the mAP by 1.32% and 1.47% on KITTI and UA-DETRAC, respectively. Nevertheless, it shows a processing speed of 35.07FPS and a more significant improvement in accuracy (90.36% vs. 85.71%) on the Object-Mask, a dataset that focuses on occlusion conditions. Therefore, the proposed algorithm is more suitable for autonomous driving applications.

在自动驾驶过程中，复杂的背景和多个目标之间的相互遮挡，阻碍了检测器的正确判断和漏检。当近距离目标再次被捕获时，车辆可能无法及时做出反应而导致致命事故。因此，在辅助系统的应用中，需要一种能够在复杂背景下准确识别部分遮挡目标，并对完全遮挡目标进行短期跟踪和预警的模型。本文提出了一种基于YOLOv3在支持实时操作的同时提高检测精度的方法，对那些完全被遮挡的物体实现实时警告。首先，我们通过逐类 K 均值聚类获得更合适的先验帧设置。针对原有CBAM的maxpool操作容易引入背景噪声的问题，我们提出了AS-CBAM（Adaptive Selection Convolutional Block Attention Module）并创新性地结合HDC（Hybrid Dilated Convolution）最大化感受野并微调特征. 1×1卷积操作用于抑制参数量的增加。在本研究中，DIOU-NMS 被用来代替传统的 NMS。此外，引入了一种基于卡尔曼滤波和匈牙利匹配的跟踪算法，以提高系统识别被遮挡物体的能力。与传统的YOLOv3相比，所提出的方法在KITTI和UA-DETRAC上的mAP分别提高了1.32%和1.47%。尽管如此，它显示出 35.07FPS 的处理速度和更显着的精度提升 (90. 36% vs. 85.71%）在 Object-Mask 上，这是一个专注于遮挡条件的数据集。因此，所提出的算法更适合于自动驾驶应用。