An Improved Lightweight Network for Real-Time Detection of Potential Risks for Autonomous Vehicles

In this study, we explore advancements in 2D object detection, focusing on the well-regarded KITTI dataset used extensively in autonomous driving research. We present YOLOv8, the latest iteration of cutting-edge single-stage detectors, and detail our network enhancements designed to boost its performance. Our method integrates the BiFPN (Bifurcated Feature Pyramid Network), optimizing the fusion of semantic and localization information crucial for precise object detection. We also add a specialized tiny object detection layer to effectively handle the challenges of small-scale object detection. To further improve our model, we employ extensive dataset augmentation techniques during training, which greatly enhance the network's robustness and generalization abilities. Additionally, we introduce the MCA (Multidimensional Collaborative Attention) module, a sophisticated attention mechanism that significantly improves feature extraction. Through thorough ablation studies, we validate the effectiveness of our enhancements, especially in detecting small targets within the KITTI dataset. This comprehensive research highlights the significance of state-of-the-art technologies like YOLOv8 and demonstrates the potential of our proposed modifications to advance the area of 2D visual object recognition. And detection