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: Abnormal Object Detection in Specific Region. 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 Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

中文翻译：

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基于Mask R-CNN的特定区域异常物体检测研究

图像作为一种语义丰富的信息载体，在物流管理的实时监控中发挥着越来越重要的作用。异常对象通常与特定区域密切相关。在特定区域检测异常物体，有利于提高检测分析的准确性，从而提高物流管理水平。受这些观察的启发，我们设计了一种称为特定区域异常对象检测的方法，该方法基于 Mask R 卷积神经网络：特定区域中的异常对象检测。在该方法中，通过传统的Mask R-卷积神经网络方法得到初始实例分割模型，然后计算特定区域的区域重叠，确定每个实例的重叠比例，并将这两部分信息融合起来预测异常对象。最后，在原始图像中恢复和检测异常对象。实验结果表明，我们提出的特定区域异常对象检测可以有效识别特定区域中的异常对象，并且明显优于最先进的方法。