Multiscale object detection based on channel and data enhancement at construction sites

Hengyou Wang, Yanfei Song, Lianzhi Huo, Linlin Chen, Qiang He

Abstract: Object detection based on computer vision techniques plays an important role in the safety monitoring of large-scene construction sites. However, current object detection algorithms typically have poor performance on small targets. In this study, an enhanced multiscale object detection algorithm is developed to solve the problem of poor detection performance due to scale changes at construction sites. First, a scale-aware data automatic augmentation is defined to learn a data augmentation strategy. Then, to mitigate information loss caused by channel reduction when using feature pyramid network, we propose a method based on subpixel convolution to perform channel enhancement and upsampling, and add a bottom-up path to enhance the complete feature hierarchy with accurate localization signals in the lower layers. Experimental results show that the proposed algorithm achieves better accuracy on the construction site (MOCS) data set and the MS COCO data set. For example, compared with the Faster R-CNN detector with the ResNet-50 backbone network on the MOCS data set and MS COCO data set, the average accuracy increased by $$8.0\%$$ 8.0 % and $$1.5\%$$ 1.5 % , respectively. In particular, the average accuracy of small targets increased by $$10.3\%$$ 10.3 % and $$3.4\%$$ 3.4 % , respectively.