Ship Detection in Fog Based on MSRCP and Improved YOLOv5
Aiming at the problems of low recognition effect and high missed detection rate of small target ships in the images obtained in fog on the sea,this paper proposes an improved YOLOv5 model integrating the MSRCP algo-rithm.The MSRCP algorithm is added to the input to preprocess the image to improve the characteristics of distant ships.The improved k-means clustering method is used to design a priori box,which accelerates the convergence speed of the model and makes the anchor box and the boundary box match better.In the network part,Softpool pooling is used to replace the original Maxpool pooling,retain more image features and improve the detection accuracy of ima-ges.The map value of the improved algorithm is increased by 12%,the average recall rate is increased by 16%,and the detection speed is up to 40 frames/second.On the premise of meeting the real-time detection effect,it can better complete the ship recognition in foggy weather.
Target detectionShip recognitionShip detection in fog
NETL
NSTL
万方数据
