基于深度卷积神经网络的红外船只目标检测方法 下载: 1942次
ing at the problems of low recognition accuracy and slow recognition speed due to the fuzzy image of infrared ship targets, a classification algorithm based on deep convolution neural network (CNN) is proposed. By using the marker-controlled watershed segmentation algorithm, the connected regions in infrared ship image are extracted and the corresponding target positions of the original image are marked and normalized to extract the candidate regions. The improved AlexNet (a deep CNN model) is used for ship targets identification. The extracted candidate regions are sent to the improved AlexNet for feature extraction and prediction to obtain the final detection result. The marker-controlled watershed segmentation method can greatly reduce the number of candidate regions and reduce the classification time of deep CNN. The data of nearly one thousand infrared ship images are obtained by the laboratory-made infrared imaging system, and the simulation experiment on the dataset formed by its translation and scaling is performed. The simulation results show that the combination of the marker-controlled watershed segmentation algorithm and the deep CNN can effectively identify the ship targets. The proposed method has good performance and can identify infrared ship targets more quickly and accurately.
王文秀, 傅雨田, 董峰, 李锋. 基于深度卷积神经网络的红外船只目标检测方法[J]. 光学学报, 2018, 38(7): 0712006. Wenxiu Wang, Yutian Fu, Feng Dong, Feng Li. Infrared Ship Target Detection Method Based on Deep Convolution Neural Network[J]. Acta Optica Sinica, 2018, 38(7): 0712006.