不同日照强度下的舰船目标识别

Ship Target Recognition Under Different Sunlight Intensity

海面目标监测时,舰船目标的清晰度常随着不同的日照强度下海面光线反射强度而变化,不同的日照强度会导致舰船目标识别率不稳定,出现误判虚警率提高等问题.为此,提出基于ResNet-50的舰船目标识别算法.首先使用ResNet-50网络提取图像特征信息,并对日照强度变化前后的特征进行日照鲁棒损失约束,减小特征差异;然后采用灰度直方图计算特征统计矩的方法得到日照对比度、亮度、平滑度、信息量、三阶矩和熵6种特征,并生成新的特征向量对日照强度前后的特征再次进行日照鲁棒损失约束,削弱和约束日照变化前后亮度、对比度因素对特征的影响;最后将二者约束联合构成损失函数并进行训练,使用贝叶斯自适应超参数优化训练最佳权重.实验结果表明,针对舰船日照变化数据库的平均识别率达到90.47%,比改进前提高4.00%左右,对日照强度为25,65和80的舰船图像识别率分别提高3.14%,6.07%和16.41%,该算法对日照强度变化有着良好的约束作用,显著提升了识别率.

In the case of surface target monitoring,the clarity of the ship target often varies with the reflec-tion intensity of the sea surface under different sunlight intensity,which will lead to the unstable recognition rate of the ship target and increase the false alarm rate.For this reason,the ship target recognition algorithm based on ResNet-50 is proposed.Firstly,it uses ResNet-50 network to extract image feature information and applies sunlight robust loss constraint to the features before and after sunlight intensity change to reduce the feature difference.Then,it uses gray-scale histogram to calculate the statistical matrices of features to obtain six features:light contrast,brightness,smoothness,information,third-order matrices and entropy,and gen-erates new feature vector to apply sunlight robust loss constraint to the features before and after sunlight in-tensity change again.Finally,the two constraints are combined to form a loss function and trained to opti-mize the optimal weights using Bayesian adaptive hyperparameters.The experimental results show that the average recognition rate of the database for ship sunlight variation reaches 90.47%,which is about 4.00%higher than that before the improvement,and the recognition rate of ship images with sunlight variation of 25,65 and 80 increases by 3.14%,6.07%and 16.41%,which shows that the algorithm has a good constraint effect on sunlight variation and the recognition rate is significantly improved.