A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution

Abstract

Rapid detection of solid waste with remote sensing images is of great significance for environmental protection. In recent years, deep learning-based object detection methods have been widely studied. In contrast to regular objects such as airplanes or buildings, solid wastes commonly h ave arbitrary shapes with difficult‐to‐distinguish boundaries. In this study, a solid waste detection network with a weighted deformable convolution and a global context block based on Feature Pyramid Network (FPN) model was proposed. The designed feature extraction structure can help to enhance the boundary and shape features of solid waste. The effectiveness of the proposed method was verified on the well-known DetectIon in Optical Remote sensing images data set and further on a solid waste data set, which was collected by the authors manually. The experimental results show that the proposed method outperforms other traditional object detection methods and a maximum improvement of 5.27% was obtained compared to the FPN method.