A Light-weight Ship Detection and Recognition Method Based on YOLOv4
Tong Yue, Yu Yang, Jiaming Niu · 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) · 2021
Ship detection and recognition based on deep learning often needs high standard hardware support while achieving high precision, which is difficult to adapt to offshore resource-limited platforms. Trying to solve this problem, this paper adopts the one-step target detection model YOLOv4 as the framework and applies a comprehensive network simplifying method. Firstly, this method applies different lightweight backbone networks in the framework to obtain the ideal Mobilenetv2-YOLOv4 network, and then conducts sparse training based on the scale factor of the batch normalization layer. Finally, it selects an appropriate threshold to prune unessential channel, which obtains a light-weight ship detection neural network for ship detection and recognition. The average accuracy of the network for detecting and identifying targets of 8 types of ships reaches 92.8% on average, the real-time detection speed is 37 frames per second, and the detection efficiency is 70% higher than that of the original network, which is capable of real-time detection under the condition of limited resources. The results also show that under simple tasks, appropriate methods can effectively compress the network parameters and computations while maintaining accuracy.