Optimizing Vehicle Detection Through YOLO-Based Deep Learning Strategies

Abstract

Vehicle detection plays a crucial role in automotive electronic systems and automated driving systems, involving the recognition of specific vehicle types on roads. Addressing the issues of low detection accuracy and slow recognition speed in existing vehicle detection methods, this paper proposes an enhanced vehicle detection model based on YOLO. To account for the varying scales of vehicles and their impact on the detection model, a normalization method is utilized to improve the calculation of prior anchor frame dimensions. Additionally, a multi-layer feature fusion strategy is implemented to enhance the network's feature extraction capabilities by eliminating redundant high-level convolutional layers. Experimental results on the validation dataset demonstrate that the proposed method achieves a mean average precision (mAP) of 90.69% and a mean frames per second (fps) of 19.1, showcasing its effectiveness.