Research on abnormal object detection in specific region based on Mask R-CNN. (18th May 2020) - Xiong, Haitao; Wu, Jiaqing; Liu, Qing; Cai, Yuanyuan
As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: A bnormal O bject D etection in S pecific R egion. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed A bnormal O bject D etection in S pecific R egion can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.