Object detection in autonomous driving scenarios represents a significant research direction within artificial intelligence. Real-time and accurate object detection and recognition are crucial in ensuring autonomous vehicles’ safe and stable operation. In recent years, the continuous introduction of the YOLO series of algorithms and their enhanced models has led to remarkable performance in autonomous driving object detection. From YOLOv1 to YOLOv12, detection accuracy has improved significantly, with mAP increasing from approximately 63.4% to over 80% and inference speed exceeding 100 FPS in lightweight versions such as YOLOv8n and YOLOv10. This paper reviews the YOLO algorithm and its application in object detection in autonomous driving scenarios. Firstly, the development and distinctions among the YOLO series of detection algorithms are explained, and their performance is analyzed. Secondly, the strategies for improving YOLO-based models across the input, feature extraction, and prediction stages are summarized. Thirdly, the research status and application of the YOLO algorithm in autonomous driving object detection are elaborated upon from the perspectives of traffic vehicles, pedestrians, traffic signs, traffic lights, and lane lines, with comparisons and analyses of performance metrics such as accuracy and real-time performance. Finally, considering the current challenges in autonomous driving object detection, the development trajectory and prospects of the YOLO algorithm are summarized and discussed.

Accurate real-time detection of small traffic objects remains a critical challenge for onboard vision-based traffic perception, particularly under conditions of weak texture, scale variation, occlusion, and limited computational resources. To address these challenges, this paper proposes DER-YOLO, a lightweight small-object-oriented detector built upon YOLO11n, specifically designed for complex traffic scenes. DER-YOLO introduces stage-wise feature calibration across the backbone, neck, and pre-head stages to enhance small-object representation. First, a Decoupled Global Context C3k2 (DGC-C3k2) module strengthens contextual representation for weak and low-saliency traffic objects after local feature extraction. Second, an ECA-guided Cross-scale Adaptive Fusion (ECAF) module adaptively balances high-level semantic cues and shallow high-resolution details to improve multi-scale feature interaction. Third, a Refined Large Selective Kernel (RLSK) module refines high-resolution spatial responses before the P3 detection head, enhancing small-object localization. Extensive experiments on KITTI and BDD100K demonstrate that DER-YOLO improves detection accuracy while maintaining real-time inference. On KITTI, it achieves 86.95% mAP@0.5 and 60.89% mAP@0.5:0.95, with small-object AP@0.5:0.95 increasing from 27.0% to 29.3%. On BDD100K, it achieves 55.05% mAP@0.5 and 29.21% mAP@0.5:0.95, with small-object AP@0.5:0.95 increasing from 12.6% to 15.2%. With 2.744 M parameters, 7.401 GFLOPs, and over 100 FPS, DER-YOLO provides an effective and lightweight solution for real-time small-object detection in onboard traffic perception scenarios.