Open Access

ARTICLE

Vehicle Target Detection Method Based on Improved SSD Model

Guanghui Yu¹, Honghui Fan¹, Hongyan Zhou¹, Tao Wu¹, Hongjin Zhu^{1, *}

1 College of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, China.

* Corresponding Author: Hongjin Zhu. Email: .

Journal on Artificial Intelligence 2020, 2(3), 125-135. https://doi.org/10.32604/jai.2020.010501

Received 07 March 2020; Accepted 06 June 2020; Issue published 15 July 2020

Abstract

When we use traditional computer vision Inspection technology to locate the vehicles, we find that the results were unsatisfactory, because of the existence of diversified scenes and uncertainty. So, we present a new method based on improved SSD model. We adopt ResNet101 to enhance the feature extraction ability of algorithm model instead of the VGG16 used by the classic model. Meanwhile, the new method optimizes the loss function, such as the loss function of predicted offset, and makes the loss function drop more smoothly near zero points. In addition, the new method improves cross entropy loss function of category prediction, decreases the loss when the probability of positive prediction is high effectively, and increases the speed of training. In this paper, VOC2012 data set is used for experiment. The results show that this method improves average accuracy of detection and reduces the training time of the model.

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Citations