Abstract
The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.
Highlights
Object detection is an important research direction in the field of computer vision.Thanks to the rapid development of deep learning technology, many detection models based on convolutional neural network (CNN) have been designed to achieve highprecision optical image target detection, such as YOLO [1], SSD [2], and Faster-RCNN [3].At the same time, the prosperity of optical image target detection technology brings hope for high-precision synthetic aperture radar (SAR) image ship detection tasks
Image ship detection problem in high-resolution nearshore scenes, many researchers have introduced some excellent CNN-based optical image detection models into SAR image ship detection [4,5,6,7]. These studies proved that the performance of CNN-based detection models on SAR ship detection tasks is much better than traditional SAR ship detection algorithm such as CFAR [8]
[3], we found that this simplified anchor-free model box, and the output of other pixels in the localization branch will be discarded. has faster convergence speed in the field of SAR ship detection, so we use it Compared with the traditional anchor-based detection model such as RetinaNet [16]
Summary
Object detection is an important research direction in the field of computer vision.Thanks to the rapid development of deep learning technology, many detection models based on convolutional neural network (CNN) have been designed to achieve highprecision optical image target detection, such as YOLO [1], SSD [2], and Faster-RCNN [3].At the same time, the prosperity of optical image target detection technology brings hope for high-precision synthetic aperture radar (SAR) image ship detection tasks. Since simple morphological filtering or traditional detection methods cannot well solve the SAR image ship detection problem in high-resolution nearshore scenes, many researchers have introduced some excellent CNN-based optical image detection models into SAR image ship detection [4,5,6,7]. These studies proved that the performance of CNN-based detection models on SAR ship detection tasks is much better than traditional SAR ship detection algorithm such as CFAR [8].
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