# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 1 Introduction
For instance, [PERSON] et al. [23] utilized a blend of downsampled shallow layers along with upsampled deeper layers to create region proposals. [PERSON] et al. [24] applied a technique involving high-frequency sub-band channel fusion to enhance ship features in SAR imagery. Meanwhile, [PERSON] et al. [25] enhanced the YOLOv5 model with a bidirectional feature pyramid network to improve the ability to detect ships of various scales. In another advancement, [PERSON] et al. [26] introduced the SAS-FPN module, merging porous spatial pyramid pooling with shuffled attention strategies to minimize the loss of ship features. Furthermore, [PERSON] et al. [27] achieved rotation-invariant properties of the SAR ship hull in the frequency domain by applying a Fourier transform to the multi-scale features derived from the Feature Pyramid Network (FPN). Additionally, [PERSON] et al. [28] formulated a novel loss function integrated with GIoU to diminish the network's sensitivity to scale variations, thereby focusing on enhancing the algorithm's performance in detecting ship targets within SAR imagery characterized by complex backgrounds. Despite the availability of advanced deep learning methods for ship detection, SAR imagery, known for its unique radar imaging features, presents considerable interpretation challenges. Factors such as self-scattering mechanisms, terrain interference, and atmospheric noise contribute to issues like salt-and-pepper noise, blurred edges, and shape distortion during the imaging process, posing significant hurdles in accurately locating and identifying ship shapes [29]. Detecting ships using SAR imagery presents numerous challenges: (1) In SAR imagery, the majority of ships are depicted by only a small cluster of pixels, thereby imposing high requirements on the detection algorithm's capability to recognize small targets. (2) The existence of salt-and-pepper noise and radar interference within SAR images leads to the blurring of boundaries, ghosting effects, and misplacement of the identified ships. This significantly undermines the reliability of target identification and localization. (3) The visual interpretation of SAR imagery is significantly dependent on the expertise of specialists in the field. A notable challenge in this domain is that considerable inconsistencies in the annotation quality of datasets complicate the ship detection situation. In order to tackle the previously mentioned challenges, this paper introduces a Multi-Location Cross-Attention Ship Detection Network (LCAS-DetNet), which is based on the YOLO architecture [18]. This architecture comprises a feature extraction component, an intermediate neck layer, and a detection head. Furthermore, to mitigate the effects of noise interference in detection tasks, we present the Multi-Location Cross-Attention (MLCA) framework. The MLCA strategy integrates both global and local perspectives to pinpoint ship locations with high precision. It employs a dual-branch structure that acts as a filter during feature extraction, identifying relevant targets amid perceptual features, thus eliminating non-informative noise and random noise points. To enhance the accuracy in determining the spatial location of ships, our approach maintains the spatial coordinates of the target across multiple scales leveraging a residual structure for location data. Notably, drawing inspiration from Wise-IoUv3 [30], this research integrates a focusing mechanism into the loss calculation process. This is achieved by adopting the degree of deviation as a weight factor, which helps in minimizing the influence of dataset quality discrepancies on the gradient updates. The main contributions are as follows:  
1. We propose a Multi-Location Cross-Attention (MLCA) to enhance the network's focus on key regions, suppress salt-and-pepper noise interference, and highlight ship features. 2. An multi-scale cascaded feature extractor with spatial coordinates is proposed. It is used for extracting ship features while retaining the multi-scale spatial coordinates of the ships. 3.