Object Detection Based on Improved YOLOX-S Model in Construction Sites

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.

Keywords

• object detection; construction sites; structural re-parameterization; large kernel attention; yolox-s