Implementation and Optimization of Target Detection based on Multi-core DSP

As an important branch in the field of image processing, target detection requires more and more real-time algorithm with its wide application in industry, agriculture, medical and military industries, which also puts forward higher requirement for the optimization and application of embedded platform. There are two kinds of target detection algorithms: traditional detection algorithm and deep-learning-based detection algorithm. In the embedded platform, due to the limitation of hardware performance, the detection algorithm based on deep learning is difficult to realize because of high complexity. Therefore, the improvement, optimization and transplantation of traditional algorithms have become hot spots. In this paper, a detection algorithm based on Canny edge feature matching is proposed, and multi-core parallel development of TMS320C6678 platform is carried out. Parallelism of the algorithm is improved by using methods such as image chunking, vectorized data packaging, optimization of data flow, etc. By connecting the video board based on FPGA for physical verification, the speed of the algorithm after multi-core optimization is increased by 7.6 times, which has certain engineering practical significance.