关键词: 目标检测, 卷积神经网络, 数据增强, YOLO

Abstract: YOLO series object detection algorithms exist unbalanced accuracy and computational cost, as well as insufficient model generalization. To address these issues, this paper proposes a high accuracy and fast vehicle and pedestrian detection model based on YOLO, called YOLO-Day Night and Fast（YOLO-DNF）, which detects vehicle and pedestrian under different lighting scenarios. This paper analyses the effects of convolutional structure and network depth on the feature extraction capability and calculation cost of the backbone in relation to the convolutional neural networks used in the mainstream detection models today. Then this paper proposes an ACNet network with low computational cost and high feature extraction capability by selecting the convolutional structure arrow-block and CSP-Block for different levels of the network and determining the depth of the network by quantifying the computational cost of stacked units. In addition, the article analyses the difference in luminance between daytime and nighttime images and introduces a data enhancement strategy of HSV domain perturbation and luminance processing to enhance the detection accuracy of the model and improve the problem of insufficient generalization of the model. The experimental results show that the YOLO-DNF model achieves 32.8% detection accuracy of full time mAP at 24.36 frames per second after training in the training set of SODA10M dataset containing only daytime images, which exceeds the current mainstream detection models in terms of detection accuracy and speed. The nighttime accuracy reaches 27.7%, which improves the nighttime detection capability of the model and extends the detection application scenarios of the model.

Key words: object detection, convolutional neural network, data augmentation, YOLO