基于深度卷积神经网络的红外船只目标检测方法

Infrared Ship Target Detection Method Based on Deep Convolution Neural Network

针对红外船只图像较模糊导致的识别率低、识别速度慢等问题,提出了一种基于深度卷积神经网络(CNN)的检测算法。首先采用标记分水岭分割算法提取红外船只图像中的连通区域,并对原图相应的目标位置进行标记和归一化处理,提取候选区域。采用改进的AlexNet(一种深度CNN模型)进行船只目标识别,将提取的候选区域送入改进的AlexNet进行特征提取和预测,得到最终检测结果。分水岭方法可大大减少候选区域检测时间,以及减少深度CNN识别时间。利用实验室自制的红外成像系统获取近千张红外船只图像数据,并对其平移缩放形成的数据集进行仿真实验。结果表明,标记分水岭与深度CNN的结合,可有效识别船只目标,所提方法具有良好的性能,能够更加快速准确地识别红外船只目标。

Aiming 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.