A Sample Balance-Based Regression Module for Object Detection in Construction Sites
【Abstract】
Object detection plays an important role in safety monitoring, quality control, and productivity management at construction sites. Currently, the dominant method for detection is deep neural networks (DNNs), and the state-of-the-art object detectors rely on a bounding box regression (BBR) module to localize objects. However, the detection results suffer from a bounding box redundancy problem, which is caused by inaccurate BBR. In this paper, we propose an improvement of the object detection regression module for the bounding box redundancy problem. The inaccuracy of BBR in the detection results is caused by the imbalance between the hard and easy samples in the BBR process, i.e., the number of easy samples with small regression errors is much smaller than the hard samples. Therefore, the strategy of balancing hard and easy samples is introduced into the EIOU (Efficient Intersection over Union) loss and FocalL1 regression loss function, respectively, and the two are combined as the new regression loss function, namely EFocalL1-SEIOU (Efficient FocalL1-Segmented Efficient Intersection over Union) loss. Finally, the proposed EFocalL1-SEIOU loss is evaluated on four different DNN-based detectors based on the MOCS (Moving Objects in Construction Sites) dataset in construction sites. The experimental results show that the EFocalL1- SEIOU loss improves the detection ability of objects on different detectors at construction sites.
【Author】
Xiaoyu Wang, Hengyou Wang, Changlun Zhang, Qiang He, Lianzhi Huo
【Journal】
Applied Sciences(IF：1) Time：2022-07-04
