Abstract
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.
Highlights
In the background of reform and opening-up policy in China, fully developed, modern information technology has gained explosive development and application
From the results in the figure, we find that (1) the performance of not considering and wholly considering of overlapping ratio is worse than using specify threshold t to filter object for handling, which illustrates that to detect the abnormal object, if or if not considering specific region can handle only one aspect of the detection and using both can improve performance of abnormal object detection; (2) the performance of consideration of overlapping ratio is effective and stable when t increases from 0.3 to 0.6, which means addressing the usage of relative overlapping ratio between detected objects and specific region
The method of object detection is mainly used in logistics management to identify the goods in logistics transportation
Summary
In the background of reform and opening-up policy in China, fully developed, modern information technology has gained explosive development and application. Qiao et al proposed an instance segmentation method based on Mask-R-CNN deep learning framework, which is used to solve the problem of object instance segmentation and contour extraction in the actual environment.[8] This method mainly includes the following steps: key frame extraction (detecting the huge moving frame of the object), image enhancement (reducing the influence of light and shadow), object segmentation, and abnormal object contour extraction. Mask R-CNN improves the speed and accuracy and makes the instance segmentation more accurate.[10] in the prediction stage, the effect of using Mask R-CNN to identify the conveyor belt is not very good, so it is not possible to use Mask R-CNN directly to segment instances of the conveyor belt For these reasons, this article designs an Abnormal Object Detection in Specific Region (AODinSR) based on Mask R-CNN. The application focuses on detecting normal, potential abnormal, and abnormal handling goods according to different regions, focusing on abnormal handling goods, and automatically identifying the falling phenomenon of goods
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