An Efficient Ship-Detection Algorithm Based on the Improved YOLOv5

Abstract

Aiming to solve the problems of large-scale changes, the dense occlusion of ship targets, and a low detection accuracy caused by challenges in the localization and identification of small targets, this paper proposes a ship target-detection algorithm based on the improved YOLOv5s model. First, in the neck part, a weighted bidirectional feature pyramid network is used from top to bottom and from bottom to top to solve the problem of a large target scale variation. Second, the CNeB2 module is designed to enhance the correlation of coded spatial space, reduce interference from redundant information, and enhance the model’s ability to distinguish dense targets. Finally, the Separated and Enhancement Attention Module attention mechanism is introduced to enhance the proposed model’s ability to identify and locate small targets. The proposed model is verified by extensive experiments on the sea trial dataset. The experimental results show that compared to the YOLOv5 algorithm, the accuracy, recall rate, and mean average precision of the proposed algorithm are increased by 1.3%, 1.2%, and 2%, respectively; meanwhile, the average precision value of the proposed algorithm for the dense occlusion category is increased by 4.5%. In addition, the average precision value of the proposed algorithm for the small target category is increased by 5% compared to the original YOLOv5 algorithm. Moreover, the detection speed of the proposed algorithm is 66.23 f/s, which can meet the requirements for detection speed and ensure high detection accuracy and, thus, realize high-speed and high-precision ship detection.