# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 1 Introduction  
The Synthetic Aperture Radar (SAR) system boasts unparalleled capabilities for all-weather surveillance, offering distinct advantages when compared to traditional optical remote sensing satellites for terrestrial observation [1; 2]. Variations in the brightness of SAR imagery are intricately linked to the backscatter coefficient of the target area, mirroring critical physical parameters such as surface texture, soil moisture, and the complex dielectric constant. The application of SAR technology has shown exceptional promise in various fields including marine environmental monitoring, geological surveys, agricultural development, emergency management, and naval reconnaissance [3; 4; 5; 6]. Ship detection is pivotal for ensuring maritime safety and providing essential data support. However, the utility of optical imagery is significantly hampered by its inherent limitations, chiefly, its inability to facilitate real-time monitoring of vessels during nocturnal hours. Moreover, optical imagery often fails in conditions of sea fog, densecloud coverage, rainfall, and various extreme or adverse weather scenarios, leading to a complete loss of the detection targets in the imagery [7]. In contrast, SAR boasts remarkable penetration capabilities and provides consistent monitoring across all weather conditions, be it day or night, under cloud cover, or amidst rainfall. The inherent benefits of SAR technology effectively offset the shortcomings associated with optical satellites, thereby delivering a robust, continuous, and reliable data source for the enhancement of real-time ship monitoring processes. In the past, there have been a large number of excellent solutions in the field of machine learning and machine vision, which have been used for ship identification from the perspective of data processing and image processing, such as Support Vector Machines (SVMs) [8], threshold segmentation [9], statistical feature methods [3], and various manually crafted feature-based methods [10; 11]. Nevertheless, these approaches exhibit limited performance in scenarios characterized by complex environmental backgrounds, ship deformations, and prevalent noise [12]. Furthermore, while these traditional methods might show exceptional capabilities in certain specific scenarios, they suffer from a lack of versatility and adaptability across diverse situations [13]. Deep learning has proven adept at navigating the intricacies of object detection tasks, showcasing remarkable adaptability in complex scenarios [14; 15]. Its proficiency in executing multiple classification detections, alongside unparalleled accuracy and data processing capabilities, has propelled its application in the analysis of Synthetic Aperture Radar (SAR) imagery. Currently, these techniques have yielded substantial improvements in the domain of ship detection using SAR images [16]. Predominantly, general object detection algorithms are classified into two broad categories: one-stage and two-stage detection methods. One-stage detection algorithms, such as SSD [17] and YOLO [18], approach detection as a comprehensive end-to-end regression challenge. Though they compromise on the accuracy of detecting smaller objects, these algorithms exhibit a significant advantage in real-time detection capabilities. On the other hand, classical two-stage detection algorithms, such as R-CNN [19], Faster R-CNN [20], Mask R-CNN [21], and SPP-Net [22], enhance the model's sensitivity towards smaller objects. However, they introduce computational complexity that can hinder overall detection efficiency. Detecting ships on water surfaces demands algorithms that deliver both high-speed and robust performance. Given the typical image characteristics obtained through satellite remote sensing, the target ship usually occupies only a minor portion of the overall pixel count, resulting in its identification as a small target. To this end, a vast array of detection algorithms specifically tailored for ship identification has been introduced in recent research.