改进YOLOX-S模型的施工场景目标检测

doi:10.3778/j.issn.1673-9418.2205012

摘要/Abstract

摘要： 现有YOLOX-S模型在施工环境干扰下目标检测平均精准率（AP）偏低，不能较好满足实际应用需要。针对上述问题，从引入结构重参数化模块、引入卷积注意力模块、引入AdamW优化算法三方面对YOLOX-S模型进行改进。首先，利用RepVGGBlock解耦训练阶段与测试阶段的模型结构，在训练阶段模型的Backbone与Neck中构建更多残差结构，提高模型的特征提取能力。其次，利用LKA模块提取局部特征信息与长距离依赖关系，为后续计算目标边界框位置与大小提供更加有效的注意力指引，提升检测平均精准率。然后，使用AdamW优化算法替代Adam优化算法更新模型参数，进一步改良模型收敛结果，提升模型泛化能力。最后，在建筑工地运动目标数据集（MOCS）上进行实验，结果表明，改进YOLOX-S模型检测所有目标的平均精准率提升3.3个百分点，检测大目标、中目标、小目标的平均精准率分别提升3.2、2.3、2.2个百分点。同时，改进YOLOX-S模型计算代价未明显增加，可在实时运行的同时更好满足施工场景下对目标检测平均精准率的需要。

关键词: 目标检测, 施工场景, 结构重参数化, 大核注意力, YOLOX-S

Abstract: The existing YOLOX-S model has a low object detection average precision (AP) under the complex environmental disturbance in construction sites, which cannot well meet the needs of practical applications. In view of the above problems, the YOLOX-S model is improved from three aspects: the introduction of structural re-parameterization module, the introduction of convolutional attention module, and the introduction of AdamW optimization algorithm. Firstly, RepVGGBlock is used to decouple the model structure of the training phase and the testing phase. More residual structures are built in Backbone and Neck in the training phase to improve the model??s feature extraction capability. Secondly, the LKA (large kernel attention) module is used to extract local feature information and long-distance dependencies, providing more effective attention guidance for the subsequent calcu-lation of the position and size of bounding boxes, and improving the detection average precision. Thirdly, AdamW instead of Adam optimization algorithm is used to update the model parameters, which can further improve the model convergence results, and improve the model generalization ability. Finally, experimental results are carried out on the MOCS (moving objects in construction sites) dataset, which show that the improved YOLOX-S model??s average precision of detecting all targets is increased by 3.3 percentage points. And the average precision of detecting large objects, medium objects and small objects is increased by 3.2, 2.3, and 2.2 percentage points, respectively. At the same time, computational cost of the improved YOLOX-S model does not increase significantly, which can better meet the needs of object detection average precision in construction sites under the condition of real-time requirements.

Key words: object detection, construction sites, structural re-parameterization, large kernel attention, YOLOX-S

胡皓, 郭放, 刘钊. 改进YOLOX-S模型的施工场景目标检测[J]. 计算机科学与探索, 2023, 17(5): 1089-1101.

HU Hao, GUO Fang, LIU Zhao. Object Detection Based on Improved YOLOX-S Model in Construction Sites[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(5): 1089-1101.

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