CRMNet: Development of a Deep-Learning-Based Anchor-Free Detection Method for Illegal Building Objects

Abstract

Illegal construction poses a safety hazard to both cities and people and affects the social stability and long-term stability of the country. Therefore, it is important to detect illegal buildings as early as possible. However, current illegal building detection methods generally suffer from either detection cycles or low detection accuracies. To solve these challenges, this study adopts an unusual method that detects illegal building objects to prevent illegal building behavior. A detection model, CRMNet, which is based on the anchor-free detection model CenterNet, and dataset for illegal building objects are proposed. ResNet50 is selected as the backbone for extracting futures after weighing the computational cost and detection accuracy. Furthermore, Mish, a new activation function, is used to improve the identification accuracy of illegal building objects. Experimental results show that the mean average precision (mAP) of the proposed detector on the illegal building object dataset reached 88.16%, which is higher than that of other popular object detection methods. Additionally, in contrast to mainstream target detection methods, the proposed detection method has fewer parameters and a higher detection accuracy, which can be better applied to mobile devices and smart devices.