Abstract
Vision-based detection methods often require consideration of the robot’s sight. For example, panoramic images cause image distortion, which negatively affects the target recognition and spatial localization. Furthermore, the original you only look once method does not have a reasonable performance for the image recognition in the panoramic images. Consequently, some failures have been reported so far when implementing the visual recognition on the robot. In the present study, it is intended to optimize the conventional you only look once algorithm and propose the modified you only look once algorithm. Comparing the obtained results with the experiment shows that the modified you only look once method can be effectively applied in the graphics processing unit to reach the panoramic recognition speedup to 32 frames rate per second, which meets the real-time requirements in diverse applications. It is found that the accuracy of the object detection when applying the proposed modified you only look once method exceeds 70% in the studied cases.
Highlights
Service robots are getting popular in numerous applications at diverse industries
It should be indicated that the detection frame rate per second (FPS), the accuracy rate, and the average overlap rate are considered as comparison parameters
Considering the significant advantages of the M-you only look once (YOLO) detection speed, it performs more practically in terms of robot vision detection. With these comparisons shown above in section A and section B, it could be concluded that the modified YOLO (M-YOLO) method is a fast, accurate object detector, which is ideal for robot vision applications
Summary
Service robots are getting popular in numerous applications at diverse industries. The robot needs to recognize the position of objects, which require the use of sensors and image recognition approaches.[1] Involving the object detection methods, a remarkable research model[2] in the field of computer vision roars across the horizon. Vision-based detection methods often require consideration of the size of the camera sight. Considering this drawback, the panoramic image plays an important role in the object detection[16,17,18] and spatial positioning of the robot. The original YOLO image recognition cannot be applied to this picture size, which causes very poor performance of the object detection. It is expected to improve the detection accuracy by modifying the network structure to achieve a high recognition accuracy in the real time
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