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An adaptive algorithm for the detection of burrs and cavities on a workpiece is proposed for the beburring process. The conventional force control method for the deburring process has the inherent characteristic of leaving the deburred surface or edge as an imprint of the original and can not distinguish the position deflection of the end-effector and the larger burrs. By the adaptive algorithm, the reference cutting force in normal direction and the feed-rate can be adjusted automatically according to the variation of the burr size to get smooth surface. A process force model considering the burrs effect is developed to predict the cutting force. Furthermore, the proposed algorithm is integrated into the impedance control for the deburring operation. The simulation experiment has shown that the adaptive algorithm is effective to determine the existence of the burrs, obtain the desired contour and improve the machining efficiency.

The paper’s primary purpose is to optimize the performance (speed/accuracy) of vehicle detection. The vehicle dataset Vehicle2020 used in this paper is divided into ten different vehicle classes. Intersection over Union (IoU) is usually used as a standard to evaluate the accuracy of vehicle detection in a specific dataset. However, IoU as a performance of the object detection algorithm is still shortcomings. IoU is further improved and called a new algorithm EIoU. Finally, the neural network structure was redesigned, which was called VehicleNet. The experimental results show that EIoU as a performance evaluation algorithm used the vehicle detection framework can improve the performance of vehicle detection. Using the algorithm of this paper shows the performance superiority of vehicle detection.

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