用于内河船舶目标检测的单次多框检测器算法

Single shot multibox detector for ships detection in inland waterway

针对传统目标检测算法在内河水运环境受外界条件影响过大的问题,本文提出了基于单次多框检测器的内河船舶目标检测方法。单次多框检测器模型基于卷积神经网络,使用全图各个位置的多尺度区域特征进行回归,使图像可以直接作为网络的输入,避免了由于波浪、树叶晃动等外界因素产生的误检。同时,对于内河船舶样本不足的问题,应用样本增强和迁徙学习的方法训练船舶目标检测的网络模型,有效缓解了训练过程中的过拟合现象,取得了较好的检测效果。经内河不同地区的多组船舶视频检测表明:此方法具有更好的鲁棒性和更低的误检率,船舶的识别率均超过了90%,比传统的背景建模算法提高16%以上。

To avoid the excessive influence of external conditions on inland waterway environment by the traditional object detection algorithm,i.e.,background modeling,a new method based on single shot multibox detector(SSD)is proposed for ship detection.The SSD model is based on the convolution neural network and uses the multi-scale regional features of the whole map to regress,so that the image can be used directly as the input of the network and false detection of external factors such as waves and leaf shaking can be avoided.Due to the shortage of ship samples in inland waterways,the method of data enhancement and migration learning was used to train the ship detection model,and this effectively alleviated the over-fitting phenomenon during the training process and yielded better detection results.A video detection of multiple groups of ships in different areas of the inland river shows that this method has better robustness and lower false detection rate than the traditional modeling algorithm.The ship recognition rates were over 90%,which was 16%higher than that of the traditional modeling algorithm.