Ship target detection is an important guarantee for the safe passage of ships on the river. However, the ship image in the river is difficult to recognize due to the factors such as clouds, buildings on the bank, and small volume. In order to improve the accuracy of ship target detection and the robustness of the system, we improve YOLOv3 network and present a new method, called Ship-YOLOv3. Firstly, we preprocess the inputting image through guided filtering and gray enhancement. Secondly, we use k-means++ clustering on the dimensions of bounding boxes to get good priors for our model. Then, we change the YOLOv3 network structure by reducing part of convolution operation and adding the jump join mechanism to decrease feature redundancy. Finally, we load the weight of PASCAL VOC dataset into the model and train it on the ship dataset. The experiment shows that the proposed method can accelerate the convergence speed of the network, compared with the existing YOLO algorithm. On the premise of ensuring real-time performance, the precision of ship identification is improved by 12.5%, and the recall rate is increased by 11.5%.
A New Method to Measure Real-Time Performance Parameters of Embedded Operating Systems Based on Benchmark Program
