YOLOX-Lite: an efficient model based on YOLOX for object detection

Zhouyu Gu; Yuecheng Yu; Anqi Ning; Wanye Gu

doi:10.1117/12.2678802

14 April 2023 YOLOX-Lite: an efficient model based on YOLOX for object detection

Zhouyu Gu, Yuecheng Yu, Anqi Ning, Wanye Gu

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 1263406 (2023) https://doi.org/10.1117/12.2678802
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

As a high precision target detection model, YOLOX still has the disadvantage of slow detection speed and is difficult to apply to the work scene with limited computing resources. Thus, an efficient target detection network, called YOLOX-Lite, which balances detection speed and detection accuracy, is proposed in this paper. Firstly, the mixed efficient channel attention module is designed to realize the adaptive refinement of spatial features and channel features in the network. Then the feature extraction ability of YOLOX-Lite network can be improved. Secondly, the optimized MobileNetv3 is used as the backbone network to replace Darknet53, so as to significantly reduce the computational complexity of the backbone network in feature extraction. Finally, efficient down_ sampler with focus is designed, which can efficiently integrate the low dimensional details in the backbone network with the high-dimensional semantic information in the neck layer. At the same time, when constructing the neck layer, the depth separable convolution is combined with PANet. It can reduce a lot of computing overhead caused by excessive multiplexing of standard convolutions. The experimental results on PASCAL VOC and TT100K datasets show that the mAP values of YOLOX Lite are 84.3% and 88.1% respectively, and the FPS value reaches 56.7. Thus, while ensuring the network detection accuracy, the detection speed of YOLOX Lite is increased by about 17% compared with the original YOLOX.

Citation Download Citation

Zhouyu Gu, Yuecheng Yu, Anqi Ning, and Wanye Gu "YOLOX-Lite: an efficient model based on YOLOX for object detection", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 1263406 (14 April 2023); https://doi.org/10.1117/12.2678802

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KEYWORDS

Object detection

Convolution

Feature extraction

Neck

Detection and tracking algorithms

Design and modelling

Feature fusion

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