SST-YOLO: An Improved Autonomous Driving Object Detection Algorithm Based on YOLOv8

As autonomous driving technology progresses, efficient and accurate object detectors are able to detect pedestrians, vehicles, road signs, and obstacles in real time, thereby enhancing driving safety and serving as a part of autonomous driving. However, the performance of such object detectors is limited and cannot be leveraged to satisfy a modern autonomous driving system. To address this issue, we develop an object detection network for autonomous driving scenarios, SST-YOLO, which is based on YOLOv8. Specifically, we propose a Sobel convolution & convolution (SCC) to enhance the backbone network of YOLOv8 and perform more full feature extraction. In addition, we replace the original path aggregation feature pyramid network (PAFPN) with a small object augmentation pyramid network (SOAPN) to solve the small object detection problem. For regression accuracy and classification robustness, and thereby to increase the performance in a complex driving scenario, we use a Task-Adaptive Decomposition & Alignment Head (TADAHead) to replace the old YOLOv8 detection head. Experiments on the public autonomous driving dataset KITTI also show that our proposed method outperforms the baseline YOLOv8 model. Compared with the baseline results, the detection accuracy ranges from 65.1% to 68.2%, which indicates that the proposed SST-YOLO network can achieve object detection for autonomous cars.