MP-YOLO: multidimensional feature fusion based layer adaptive pruning YOLO for dense vehicle object detection algorithm

Abstract

In recent years, artificial intelligence technology has been applied in the research and development of autonomous vehicles. However, the high energy consumption of artificial intelligence models and the high precision requirements of object detection in autonomous driving have led to a stagnation in the development of autonomous vehicles. To alleviate the above problems, we optimize YOLOv8 and propose a lightweight vehicle object detection algorithm, MP-YOLO (Multidimensional feature fusion and layer adaptive pruning YOLO), to adapt to edge devices with limited storage while meeting the requirements for detection accuracy. Firstly, two multi-scale feature fusion modules, MSFB and HFF, are proposed to merge features of different dimensions, enhancing the model’s feature learning capability. Secondly, a detection head at a scale of 160*160 is added to improve small object detection capability. Thirdly, the WIoU loss function replaces the original CIOU loss function in YOLOv8 to address the issue of high overlap among road objects. Lastly, using the Layer Adaptive Sparsity for Magnitude-based Pruning (LAMP) method to significantly reduce model size. The MP-YOLO model was tested on the latest automatic driving dataset DAIR-V2X, and the results showed that the performance of the proposed MP-YOLO exceeded the original model, with improvements of 4.7 % in AP₅₀ and 4.2 % in AP, and the model size changed from the initial 6 MB to 2.2 MB. It is superior to other classical detection models in terms of volume and accuracy, and meets the requirements of deployment on edge devices. The source code is available at https://github.com/Wang-jj-zs/MP-YOLO/tree/master.