# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 1 Introduction
### Ship Detection Using Convolutional Neural Networks  
With the extensive research of deep learning methods in recent years, convolutional neural networks have been increasingly used in the field of object detection [22; 23]. Convolutional neural networks can automatically extract deep abstract features that are difficult to represent by hand design, so they show advantages compared to traditional machine learning algorithms. At the same time, it can also reduce manual participation and save labor costs. We describe these CNN methods from two aspects: region-based (two-stage) frameworks and unified (one-stage) frameworks [24]. The region-based framework is a two-stage cascading network. One is used to generate object proposals, and the other determines whether the desired object exists. When the successful application of deep CNNs in image classification was transferred to object detection it resulted in the first region-based CNN (RCNN) detector and showed good performance [25]. In spite of achieving high object detection quality, the RCNN still has some drawbacks. Subsequently, a series of improved algorithms were derived, such as SPPNet [26], Fast RCNN [27], Faster RCNN [28], and DeFRCN [29]. In addition, the authors of [30] developed an attention mask R-CNN to detection ships. It can accurately segment ships at the pixel level by adding a bottom-up structure to the FPN structure of a Mask R-CNN. In [31], a novel deep CNN with a hierarchical selective filtering layer is proposed to detect ships with various scales. It shows a high detection accuracy and strong robustness. Nevertheless, region-based approaches are still slow and hard to optimize, and computationally expensive for current mobile/wearable devices. Therefore, researchers have begun to design unified detection strategies. This is different from two-stage frameworks, because one stage detection frameworks have a single method that does not separate the process of the detection proposal. All the computation will be encapsulated in a single CNN, thus making optimization easier [24]. Commonly used one-stage object detection algorithms are, DetectorNet [32], SSD [33], and YOLO series [34; 35; 36; 37]. Compared to the methods of R-CNN series, YOLO turns the object detection problem into a regression problem. Given the input image, YOLO is able to directly return the bounding box of the target and its classification category at multiple positions of the image utilizing regression algorithm. YOLO is a convolutional neural network that can predict multiple box positions and categories at the same time. It can achieve end-to-end target detection and recognition. The fast speed of YOLO is its greatest strength. The optimized YOLO network can output multi-scale feature maps and has accurate target detection performance. Moreover, in [38], a new and improved approach CornetNet is proposed to detect objects. The paired top-left and bottom-right keypoint is utilized to substitute a set of anchor boxes. In [39], a improvedmodel named YOLOF is proposed. They designed two key components, dilated encoder and uniform matching, which make the method achieve considerable effectiveness. Consequently, after comprehensive consideration, we improved the YOLOv4 network model to obtain refined ship detection results. After the aforementioned research, we propose a saliency-tuned offshore ship detection method. We used a density peak clustering algorithm [14] as the first preprocessing step in order to classify the image blocks of different scenes. We use different saliency methods (e.g., SR [20], CASD [21], and a histogram-based contrast method (HC) [40]) to detect the saliency of each scene image. In view of image characteristics in different scenes, different saliency algorithms have different advantages and can obtain better detection results for different scene images. Then, the saliency detection results are used as the third channels and stacked with the original panchromatic image. They are treated as input data and fed into the subsequent network.