Research on vehicle detection based on improved YOLOv8 network

TL;DR

This paper presents an improved YOLOv8-based vehicle detection method that enhances accuracy and efficiency by integrating FasterNet, CBAM attention, and a WIoU loss function, demonstrating superior detection performance in autonomous driving scenarios.

Contribution

The study introduces a novel vehicle detection approach using an improved YOLOv8 model with FasterNet backbone, CBAM attention, and WIoU loss, achieving higher accuracy and efficiency.

Findings

Achieved 98.3% detection accuracy for cars.

Improved detection speed and accuracy over original YOLOv8.

Outperformed YOLOv9 in key metrics.

Abstract

The key to ensuring the safe obstacle avoidance function of autonomous driving systems lies in the use of extremely accurate vehicle recognition techniques. However, the variability of the actual road environment and the diverse characteristics of vehicles and pedestrians together constitute a huge obstacle to improving detection accuracy, posing a serious challenge to the realization of this goal. To address the above issues, this paper proposes an improved YOLOv8 vehicle detection method. Specifically, taking the YOLOv8n-seg model as the base model, firstly, the FasterNet network is used to replace the backbone network to achieve the purpose of reducing the computational complexity and memory while improving the detection accuracy and speed; secondly, the feature enhancement is achieved by adding the attention mechanism CBAM to the Neck; and lastly, the loss function CIoU is modified to WIoU, which optimizes the detection box localization while improving the segmentation accuracy. The results show that the improved model achieves 98.3%, 89.1% and 88.4% detection accuracy for car, Person and Motorcycle. Compared with the pre-improvement and YOLOv9 models in six metrics such as Precision.