Research on object detection algorithm based on deep learning for mobile Terminal

Abstract

In order to improve object detection ability of robot, this study introduces an object detection algorithm which is based on deep learning. Firstly, a neural network that contains Convolution Layer, Pooling Layer and Fullly Connection Layer is designed to recognize whether the target object in image. Secondly, the Faster-RCNN, as a typical object detection algorithm, is used to detect the position of target object in image. Finally, the algorithm is transplanted on Raspberry Pi for deployment and testing. Results show that this algorithm can work well on Raspberry PI, and the mAP (mean average precision) and FPS (frames per second) reach 97% and 3. Meanwhile, the position of target object is accurately marked in image. Therefore, this study implies that the object detection algorithm based on deep learning can be implemented on mobile terminal and is helpful to improve object detection ability of mobile terminal.