## 2 Related Works on DL Methods of Small Object Detection  
The CNN, a deep feed-forward artificial neural network, shows its different characteristics depending on the problem types it is applied to. CNN plays many roles in detecting ships but can detect only small objects. Unsupervised machine learning (ML) approaches such as principle component analysis (PCA) and the k-method have been used to discriminate between ships and icebergs. However, ML has to be extended to DL when a large dataset volume is there. Many remote sensing researchers have used CNN differently for their problems. A research-implemented solution was introduced for marine surveillance systems to discriminate between ships and icebergs [1]. They utilized four Conv2D layers and three dense layers with a 3 \(\times\) 3 convolutional filter. They had a fully connected layer with 256 fully connected neurons. The authors used the pseudo-labeling approach since, in their dataset, only a small amount of labels were available. The transfer learning approach tackled the same problem, transferring the knowledge gained through a labeled SAR dataset to an unlabeled SAR dataset with future, present, and past information [2]. To classify icebergs and ships in multispectral satellite images, a C-Core dataset was used [3]. Two different CNNs were implemented with 561,217 and 1,134,081 parameters, respectively. Finally, those results were compared with support vector machine (SVM) results, and it was concluded that CNN had superior results in detecting ships. Iceberg detection was accomplished with the help of CNN, and it showed improved efficiency with the assistance of the Region of Interest (ROI), Sift, Surf, Threshold, and Transfer Learning [4]. Sidelobes were also included since the reflection of sidelobes may suppress large ships [5]. A constant false alarm rate was used to detect objects in ROI, followed by a parallel CNN, and in this approach, the parallel algorithm trained the model [6]. Moreover, a constant false alarm rate was also used after despeckling SAR images in supervised reinforcement learning [7]. SAR images is where CNN brought dominant accuracy results. The accuracy was improved by improving the features of objects in the training process [8]. The single-shot detector (SSD) is also used for object detection. Two major types of algorithms are widely available for object detection. Some algorithms such as RCNN, FRCNN, and Mask RCNN come under the first technique. There is a variety of CNN processing. Two detection stages occur. In the first stage, the region type of objects is expected to detect the object, and then objects are detected in those regions. The second technique is the fully convolutional approach. The you only look once (YOLO) approach and SSD are examples of this technique. These algorithms' networks are capable of detecting images in a single pass. SSD works with the help of two components. SSD components are the backbone model and the single shot (SS) head. VGG 16 was used as the backbone architecture [9], and the SSD method was treated as a baseline detector with a set of layers for the detection of objects. In this work, layers of SSD are responsible for specific scale objects. Large objects can be detected in the deeper layer, and small objects can be spotted in the shallow layer with lightweight objects [10]. Since SSD cannot detect tiny objects, the image pyramid network helped the model improve SSD's performance. Since there was a need for oriented object information, in place of a conventional region proposal, network-oriented candidate region networks were used. At the same time, the feature fusion network (FFN) enhanced spatial information. This FFN is used for fusing IPN layers with SSD layers. YOLO is a deep learning method that does not require dividing the images into blocks; the entire image can be looked at once. This speedy network is specifically designed for real-time object detection such as traffic signals, locating persons, finding animals, etc.