Lightweight Object Detection Based on Autonomous Driving

Abstract Addressing the issues of low accuracy and large size for tiny objects like pedestrians, non-motor vehicles, and vehicles in traditional object detection models for autonomous driving. An advanced variant of the YOLOv7-tiny model for automatic driving object detection is proposed in this study. The VoVEGSCSP module, designed for cross-layer integration within local networks, is introduced. The SPPCSPC module was replaced with the Space Pyramid Pool Fast (SPPF). This reduces parameters and calculations while preserving the diversity of the original feature scale. CARAFE is a lightweight universal upsampling operator that replaces nearest neighbor interpolation in the upsampling module, thereby reducing feature information loss during upsampling. Results demonstrate that on the KITTI dataset, the improved YOLOv7-tiny algorithm reduces parameters by 35.7% and computation by 34.1%, with only a 0.7% decrease in [email protected] accuracy.