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Obstacle detection method for guide system based on CE-YOLOX
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 9, Pages: 1281-1292(2023)
- 作者机构：
1.贵州大学 大数据与信息工程学院， 贵州 贵阳 550025
- 作者简介：
- 基金信息：Science and Technology Fund of Guizhou Province （Guizhou Science and Technology Foundation-ZK［2021］ Key 001）
DOI：10.37188/CJLCD.2022-0358
CLC：
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LIU Yuan, ZHANG Rong-fen, LIU Yu-hong, et al. Obstacle detection method for guide system based on CE-YOLOX. [J]. Chinese Journal of Liquid Crystals and Displays 38(9):1281-1292(2023)
LIU Yuan, ZHANG Rong-fen, LIU Yu-hong, et al. Obstacle detection method for guide system based on CE-YOLOX. [J]. Chinese Journal of Liquid Crystals and Displays 38(9):1281-1292(2023) DOI： 10.37188/CJLCD.2022-0358.
摘要
由于盲人对周围环境缺乏视觉感知力，出行一直是盲人生活中的困扰。本文提出一种基于YOLOX改进的导盲系统障碍物检测算法CE-YOLOX。首先，为了减少特征融合网络在特征通道缩减时带来的语义信息的丢失，以亚像素跳变融合模块SSF和亚像素上下文增强模块SCE来充分利用通道信息和不同尺度的语义信息，以通道注意力引导模块CAG减少多尺度特征融合带来的混叠效应。其次，为了使模型更加聚焦于有效特征，引入全局注意力机制GAM，通过减少信息弥散和放大全局交互表示来提高模型性能。然后，使用SIOU-LOSS替换原模型的位置回归损失函数IOU-LOSS，加快了边框的回归速度与精度。最后，搭建导盲系统检测平台，将所提算法移植到边缘计算设备NVIDIA Xavier NX上。实验结果表明，改进的导盲系统障碍物算法在服务器和NVIDIA Xavier NX平台上的mAP一致，提升至90.53%，相较原YOLOX模型算法提高了2.45%。在服务器上的检测速度达到75.93 FPS。本文模型在兼顾检测速度的同时提高了精度，显著优于对比算法，满足边缘计算设备的要求，具有实际的应用价值。
Abstract
Travel has always been a problem for blind people due to their lack of visual perception of the surrounding environment. This paper presents an improved obstacle detection algorithm CE-YOLOX based on YOLOX for guide system. Firstly， in order to reduce semantic information loss caused by feature fusion network when feature channel is reduced， sub-pixel hopping fusion module SSF and sub-pixel context enhancement module SCE are used to make full use of channel information and semantic information of different scales， and channel attention guide module CAG is used to reduce aliasing effect caused by multi-scale feature fusion. Secondly， in order to make the model more focused on effective features， the global attention mechanism GAM is introduced to improve the performance of the model by reducing the information dispersion and amplifying the global interactive representation. Then， the position regression function IOU-LOSS of the original model is replaced by SIOU-LOSS， which speeds up the regression speed and precision of the frame. Finally， the detection platform of the guide system is built and the proposed algorithm is transplanted to the edge computing device NVIDIA Xavier NX. The experimental results show that the obstacle algorithm of the improved guide system has the same mAP on the server and NVIDIA Xavier NX platform， which is improved to 90.53%， 2.45% higher than the original YOLOX model algorithm. The detection speed reaches 75.93 FPS on the server. The model in this paper not only gives consideration to the detection speed but also improves the accuracy， which is significantly better than the comparison algorithm. It meets the requirements of edge computing equipment and has practical application value.
关键词
导盲系统障碍物检测亚像素CEFPNGAM角度损失SIOU
Keywords
blind systemobstacle detectionsubpixelCEFPNGAMangle lossSIOU
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