## 2 Related Works on DL Methods of Small Object Detection
It shows high accuracy with the help of three essential components: residual blocks, bounding box regression, and intersection over union, which are responsible for detecting, predicting, and locating, respectively. In [11], YOLO v2 was implemented to detect ships in marine environments with feature separation and feature alignment, which outperforms conventional neural networks with deeper layers. However, the number of parameters handled was still increased. To avoid this hyperparameter issue, they reduced the number of layers. Tiny YOLO V3 [12] was used to detect small objects in aerial imagery, unlike conventional YOLO, which needs a large memory volume for better accuracy. However, tiny YOLO v3 needs a small amount of memory, even though there is a tradeoff between accuracy and memory. Another study [13] used conventional YOLO based on Darknet-53. Three-level pyramids were utilized for feature map creation. The Adam optimizer was implemented since the study used a small-size dataset. Finally, the model produced high precision. The generative adversarial network is a deep learning architecture that has the potential to work in both supervised and semi-supervised modes. GAN is composed of two models: generative and discriminative models. The generator model uses the network, which generates images by adding random objects [14] and produces an input image that is similar to the actual image. The discriminator attempts to classify whether the generated image the generator generates looks like the actual input image. A super-resolution network was integrated with a cycle model based on GAN residuals [15]. Feature pyramid networks are networks [16] designed explicitly as feature extractors. They create high-quality multi-scale feature maps, which are significantly better than conventional feature extractors utilized in the CNN. Feature pyramid networks use two different pathways: bottom-up and top-down pathways [17]. More ship detection research solutions were recently proposed over the SAR imagery dataset. A method was developed with the DL algorithm for a SAR image dataset [18]. In this work, various proactive solutions and their mitigated risk were analyzed. An improved method was developed for detecting ships with SAR images [19]. A pre-training technique was adopted with DL, especially for scarcely labeled SAR image datasets. A novelty was achieved with a coarse-to-fine method for detecting ships with an optical remote sensing (OSR) image dataset [20]. In this work, ResNet was used alongside discrete wavelet transform (DWT) to achieve higher accuracy for detecting ships as a state-of-the-art method. Yet another approach was initiated to detect small ship-like objects in complex SAR datasets, and a hybrid method model was created alongside intersection over union loss prediction [21]. Here, shape similarity was identified for small-ship object detection. A ship detection technique was developed with a spatial SAR dataset with a shuffle group for a large scale [22]. This work introduced enhancement with the DL algorithm for ship detection. A refinement network with feature balancing was used for multi-scale ship detection, and it was developed as an anchor-free method [23]. The DL algorithm used a SAR dataset for feature balancing in ship identification. A high-resolution ship detection mechanism was designed for a SAR dataset with large quantities [24]. This work developed a scattering and critical point-guided network with a core DL algorithm. Dense attention feature aggregation with CNN detected the ship over a SAR image dataset [25]. In this work, CNN was used anchor-free and featured dense attention. The modified mechanism with DL was used to develop a model for ship detection, and it was called a CenterNet++ mode [26]. A SAR image dataset was used for the seamless detection of the ship. A lightweight ship detection mechanism was introduced with YOLO v4, achieving high-speed detection of the ship over a SAR image dataset [27].