Edge devices with strict safety and reliability requirements, such as autonomous driving cars, industrial robots, and drones, necessitate software verification on such devices before operation. The human cost and time required for this analysis constitute a barrier in the cycle of software development and updating. In particular, the final verification at the edge device should at least strictly confirm that the updated software is not degraded from the current it. Since the edge device does not have the correct data, it is necessary for a human to judge whether the difference between the updated software and the operating it is due to degradation or improvement. Therefore, this verification is very costly. This paper proposes a novel automated method for efficient verification on edge devices of an object detection AI, which has found practical use in various applications. In the proposed method, a target object existence detector (TOED) (a simple binary classifier) judges whether an object in the recognition target class exists in the region of a prediction difference between the AI’s operating and updated versions. Using the results of this TOED judgement and the predicted difference, an automated verification system for the updated AI was constructed. TOED was designed as a simple binary classifier with four convolutional layers, and the accuracy of object existence judgment was evaluated for the difference between the predictions of the YOLOv5 L and X models using the Cityscapes dataset. The results showed judgement with more than 99.5% accuracy and 8.6% over detection, thus indicating that a verification system adopting this method would be more efficient than simple analysis of the prediction differences.

In this paper, near real time digital radiography system was implemented for the automatic verification of local errors between simulation plan and radiation therapy. Portal image could be acquired through video camera, image board and PC after therapy radiation was converted into light by a metal/fluorescent screen. Considering the divergence according to the distance between the source and the plate, we made a 340 340 12 cm3 basis point plate on which five rods of 4 cm height and 8 mm diameter lead (Pb) were built to display reference points on the simulator and the portal image. We converted the portal image into the binary image using the optimal threshold value which was gotten through the histogram analysis of the acquired portal image using the basis point plate. we got the location information of the iso-center and basis points from the binary image, and removed the systematic errors which were from the differences between the simulation plan and the portal image. Field size which was measured automatically by optimal threshold portal image, was verified with simulation plan. Anatomic errors were automatically detected and verified with the normalized simulation and the portal image by pattern matching method after irradiating a part of the radiation. Therapy efficiency was improved and radiation side effects were reduced by these techniques, so exact radiation treatment are expected.