## 1 Introduction  
Synthetic aperture radar (SAR) can provide high-resolution images in day-and-night and all-weather earth observation. SAR techniques have played a significant role in applications of environmental monitoring, maritime management and resource exploration. With the rapid growth of SAR imaging techniques, an increasing number of studies have considered object detection in SAR images. In this paper, we study ship detection in SAR images towards edge scenarios, where detection efficiency should be taken into account along with detection accuracy. For example, SAR is typically mounted on battery-powereddevices [1]. Due to limited energy and computation resources, any ship detector should not be computation intensive. To detect ship targets in SAR images, many algorithms taking the advantages of neural networks in feature extraction have been proposed. In early studies, different dedicated neural networks were designed towards having better detection accuracy. Recent studies noticed that efficiency is important as well as accuracy, especially in edge scenarios. A series of lightweight neural networks were then proposed, where detection accuracy and efficiency were considered together. According to our knowledge, existing lightweight neural networks for ship detection are mainly designed by pruning manually designed neural networks such as YOLO [2], Fast R-CNN [3], and MobileNet [4]. It is known that the performance of a neural network depends on its structure and the operations (e.g., standard convolution, depth-wise convolution, max pooling, average pooling, etc.) used. Thus, designs based on a well-known neural network are often not expected to have bad preliminary results. However, this methodology relies on artificial expertise and is subject to the existing neural architectures. Such a problem becomes more intractable when meeting two inconsistent targets: accuracy and efficiency. In many existing studies, although different baseline models are reduced on the premise of maintaining detection accuracy, a large amount of computation is still required. For example, in two recent YOLO-based lightweight ship detectors [5; 6], the FLOPs (floating-point operations) required are, respectively, 1.94 G and 3.53 G. In this paper, we follow a different methodology, neural architecture search (NAS), to design neural networks. After a search space is specified, the neural network design process can be done automatically, and meanwhile, more possibilities in the network design space could be explored. In addition, detection accuracy and efficiency are combined together in our neural architecture search model. Hence, it is expected to generate a neural network that is more attractive in detection efficiency and is still competitive in detection accuracy. The main body of our ship detector is composed of a backbone and a detection head. The backbone is used to extract features, and the detection head is used to identify targets. Note that most computations are required by the backbone. Clearly, the lighter the backbone is, the higher the detection efficiency that could be achieved. In order to generate a satisfactory backbone, techniques of Darts [7; 8] are adopted. We first followed the recommended settings [7; 8] to construct a ship detector. However, after testing on HRSID [9] and SSDD [10], we obtained no good results, only serious overfitting. Furthermore, we found that first conducting an architecture search by Darts and then pruning the neural network generated can hardly obtain sufficient improvements. Aiming at the problem, we customized a new architecture search model named Darts_Tiny. Compared with Darts, Darts_Tiny first prunes the super neural network used for the architecture search. In addition, a channel-pruning scheme is carried out along with the search process, and a synthetic loss function is used such that not only is the overfitting restrained, but also, the detection efficiency is enhanced. Comprehensive experiments are conducted on two datasets, HRSID [9] and SSDD [10].