Synthetic aperture radar (SAR) is an essential tool for ocean surveillance. As the main participants on the ocean, ships are the most important targets for ocean monitoring. So it is of great importance to develop ship detection algorithms for SAR sea images. The algorithms based on Convolutional Neural Network perform far better than the traditional methods based on manual features on ship detection task due to the powerful feature representation abilities. The algorithms based on Convolutional Neural Network can be divided into one-stage algorithms, and two-stage algorithms. Two-stage algorithms have high accuracy, but are relatively time-consuming. One-stage algorithms have high inference speed, but compared with two-stage algorithms, they have lower accuracy. So in this article, we proposed an modified one-stage detection algorithm to improve the accuracy of ship detection in a condition that the modified algorithm meet the real-time requirement. First, the small model of one-stage algorithm YOLOV5 is chosen as the base network to get the high inference speed. Then, to improve the accuracy of ship detection with a little increase in inference time and the model parameters, we integrate the one–layer super-resolution architecture with the simplist structure into the YOLOV5 network. Finally, we conducted the comparative experiments on our Dataset to verify the performance of modified YOLO V5. The experimental results show that the modified method has obtained an Average Precision (AP) improvement than the original YOLO V5 for detecting ships in SAR images with a little increase in inference time and the model parameters.
Synthetic aperture radar (SAR) has a special ability to work in any type of inclement weather, and is a very suitable tool for Ocean surveillance. Scene classification is an essential pre-task of other computer vision tasks for ocean monitoring. It is of great importance to develop scene classification technology of SAR sea images. Due to the excellent feature representation abilities of neural networks, the deep learning-based methods are far superior to the traditional methods based on manual features in scene classification task performance. Many lightweight classification networks have been proposed to improve the inference speed of the networks. But in comparison with ordinary CNNs, the lightweight networks have slightly lower accuracy for scene classification tasks. So in this article, we proposed an improved lightweight Convolutional Neural Network for scene classification of SAR sea images. First, in order to meet the real-time performance, we choose MobileNetv1 as the original classification network in this paper. Then, to compensate for the lack of accuracy, we use 1D asymmetric convolution kernels to strengthen each layer of the depthwise convolutions in the network. Finally, after training time, we merge the linear calculations of each layer of the network to convert it into the original structure. The experimental results show that the modified model has obtained an accuracy improvement than the original one on the scene classification of sea SAR images without extra computation.
Infrared image ship detection has important applications in military and civil affairs. Because infrared images are not easy to acquire in large quantities, deep neural networks cannot directly use infrared images for training; if the pre-trained model of visible light images is directly used for detection, the phenomenon of missed detection will be caused due to different imaging conditions. In response to this problem, this paper proposes a detection method that combines a deep convolutional neural network and salient region. Firstly, we proposed a method extracting salient region based on anchor and saliency map, then multiple new images are formed by salient regions, and the newly formed images and the original image are input to the deep convolutional neural network for parallel processing, and finally the results of the detection are integrated to produce the final detection results by the non-maximum suppression (NMS) method. The comparison results show that the method proposed in this paper can effectively reduce the rate of missed detection and thus improve the accuracy of detection.
Compared with short-term tracking, long-term tracking is a more challenging task. It need to have the ability to capture the target in long-term sequences, and undergo the frequent disappearance and re-appearance of target. Therefore, long-term tracking is much closer to realistic tracking system. But few long-term tracking algorithms have been done and few promising performance have been shown. In this paper, we focus on long-term visual tracking framework based on parts with multiple correlation filters. First of all, multiple correlation filters have been applied to locate the target collaboratively and address the partial occlusion issue in a local search region. Based on the confidence score between the consecutive frames, our tracker determines whether the current tracking result is reliable or not. In addition, an online SVM detector is trained by sampling positive and negative samples around the reliable tracking target. The local-to-global search region strategy is adopted to adapt the short-term tracking and long-term tracking. When heavy occlusion or out-of-view causes the tracking failure, the re-detection module will be activated. Extensive experimental results on tracking datasets show that our proposed tracking method performs favorably against state-of-the-art methods in terms of accuracy, and robustness.
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