Object Detection for Construction Waste Based on an Improved YOLOv5 Model
Qinghui Zhou, Haoshi Liu, Yuhang Qiu, Wuchao Zheng
Abstract
An object detection method based on an improved YOLOv5 model was proposed to enhance the accuracy of sorting construction waste. A construction waste image sample set was established by collecting construction waste images on site. These construction waste images were preprocessed using the random brightness method. A YOLOv5 object detection model was improved in terms of the convolutional block attention module (CBAM), simplified SPPF (SimSPPF) and multi-scale detection. Then, the improved YOLOv5 model was trained, validated and tested using the established construction waste image dataset and compared with other conventional models such as Faster-RCNN, YOLOv3, YOLOv4, and YOLOv7. The results show that: based on the improved YOLOv5 model, the mean average precision (mAP) on the test dataset can reach 0.9480. The overall performance of this model is better than that of other conventional models in object detection, which verifies the accuracy and availability of the proposed method.