An improved target detection algorithm based on EfficientNet. Issue 1 (July 2021)

• Record Type: Journal Article
• Authors: Wu, Tao; Zhu, Hongjin; Fan, Honghui; Zhou, Hongyan
• Is Part Of: Journal of physics. Volume 1983:Issue 1(2021)
• Publication Date: 2021-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1983/1/012017
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In order to improve the detection accuracy for small-scale targets in complex scenes, an improved target detection algorithm based on EfficientNet is proposed. Firstly, the EfficientNet network is used to optimize the DarkNet53 feature extraction network. Compressing the standard convolution with the depthwise separable convolution, and increasing the depth of the neural network with the residual network that can effectively achieve feature extraction, reduce the number of parameters and improve the detection speed. Secondly, a feature pyramid network is used to design four scales of features for multi-scale feature extraction, which improves the detection of small targets. The experimental results show that the YOLOv3 target detection algorithm improves the detection accuracy by 4.98% compared to the original algorithm on VOC dataset, which improves the detection accuracy and ensures real-time detection for small targets.