## 2 Related Work  
### Ship Detection via Neural Networks  
Convolutional neural networks are well-known for their remarkable ability of feature extraction. Since [PERSON] et al. [10] proposed a labeled open dataset, SSDD, applying convolutional neural networks to ship detection in SAR images quickly received intense attention. At the beginning, studies mainly focused on designing a neural network having better detection accuracy. Based on SSDD, [PERSON] et al. [10] first proposed a ship detector based on Faster R-CNN, and then they conducted follow-up studies for further improvements [11; 12]. To achieve better accuracy, many other solutions have been proposed. For example, [PERSON] et al. [13] leveraged feature pyramids, [PERSON] et al. [14] designed a multitask learning-based object detector, [PERSON] et al. [15] presented boundary box optimization techniques, and [PERSON] et al. [16] proposed an anchor-free method aiming at improving ship detection in high-resolution SAR images. However, all these studies did not pay enough attention to the detection efficiency, such that they might not be adaptive in an environment with limited computation resources. Recent studies on ship detection have noticed the importance of detection efficiency as well as accuracy. Aiming at the two inconsistent targets, current studies are often conducted based on existing manually designed neural networks, expecting to achieve a preliminary detection result. To obtain higher detection efficiency, either a lighter neural network backbone is used [17; 18; 19; 20] or the baseline network is tailored [5; 6; 21; 22]. Lightweight techniques sometimes decreasethe detection accuracy. Additional techniques are adopted as long as the detection accuracy needs to be compensated for [5; 6]. [PERSON] et al. [17] proposed a lightweight model based on Faster R-CNN. [PERSON] et al. [18] and [PERSON] et al. [19] proposed neural networks that took the advantages of MobileNet. In the above work, all the new designed models can achieve faster inference speed than the baseline models, such as Faster R-CNN and YOLO. More recent studies were interested in designing a lightweight ship detector based on different YOLO versions. [PERSON] et al. [23] proposed an arbitrary-oriented ship detector based on the YOLO detection framework using bi-directional feature fusion and angular classification. The amount of the parameter was 19.57 M, and the model volume was 39.4 MB. [PERSON] et al. [21] proposed Accelereated-YOLOv3 for ship detection. Compared with YOLOv3 [24], a higher detection speed is achieved by using fewer channels and convolutional layers. [PERSON] et al. [5] proposed an efficient SAR ship detector named Tiny YOLO-Lite, which was also designed based on YOLOv3. Tiny YOLO-Lite combines network pruning and knowledge distillation techniques to guarantee both efficiency and accuracy. The amount of computation is reduced to 1.94 G FLOPs, which is a fifteenth of that of YOLOv3. [PERSON] et al. [6] proposed YOLO-v4-light. Aiming at a faster detection speed, YOLOv4 [25] was tailored to reduce the model size and the number of parameters. To compensate for the loss of accuracy, YOLO-v4-light was refined for three-channel images. By cutting the amount of computation to 3.53 G FLOPs, the detection speed of YOLO-v4-light achieves rates three times faster than YOLOv4. [PERSON] et al. [26] proposed Light-YOLOv4, where the model volume, parameter size, and FLOPs can been reduced by 98.63%, 98.66%, and 91.30% compared with YOLOv4. [PERSON] et al. [22] proposed a model named Light-YOLOv5, where the amount of computation is 4.44 G FLOPs. In addition to standard YOLO versions, YOLOX [27], an anchor-free version,also received attention. [PERSON] et al. [20] proposed LPEDet based on YOLOX. To ensure higher detection efficiency, the original backbone Darknet-53 was replaced with a lighter network LCNet. Existing studies show the effectiveness of designing based on a known neural network.