# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 3 Experimental Results  
### Datasets  
HRSC2016 [32] was released by Northwestern Polytechnic University in 2016 and is a unique dataset focusing on ship detection in remote sensing images. It consists of a total of 1680 images captured from 6 renowned ports available on Google Earth. Among these images, 619 do not contain any ships and serve as background images. The image sizes range from \(300\times 300\) to \(1500\times 900\). The dataset is divided into training, validation, and test sets, with 436, 181, and 444 images, respectively. The annotation format for ship bounding boxes is oriented bounding boxes (OBB) rather than horizontal bounding boxes (HBB), and a total of 2976 ships are annotated. HRSC2016 has become one of the most widely used benchmark datasets in remote sensing detection. The dataset presents several challenges for ship detection: (1) The majority of ships are docked closely together, exhibiting a dense arrangement of ships side by side. (2) Some ships are integrated with long piers or located in shipyards or ship repair yards, where the similarity between the ships and the nearby textures is high, making their features less distinguishable. (3) Certain land buildings bear resemblance to luxury yachts, leading to potential misclassification. (4) The scale of ships varies greatly, and an image may contain an aircraft carrier as well as a very small civilian ship simultaneously. The dataset provides annotations for ship classification at three levels; however, due to their low frequency, detecting and classifying ship subcategories is challenging. Therefore, our RePaDet model focuses only on training and inferring the first-level ship category, following the approach of most researchers.  
DOTA [4; 33], publicly released by Wuhan University in November 2017, has become the most commonly used public dataset for object detection in remote sensing. DOTA-v1.0 [4] consists of 2806 images obtained from various platforms, including Google Earth, JL-1, and GF-2 satellites. The dataset contains a total of 188,282 annotated objects belonging to 15 different categories, as represented by the abbreviations in the tables presented later: airplane (PL), baseball diamond (BD), bridge (BR), ground track field (GTF), small vehicle (SV), large vehicle (LV), ship (SH), tennis court (TC), basketball court (BC), storage tank (ST), soccer-ball field (SBF), roundabout (RA), harbor (HA), swimming pool (SP), and helicopter (HC). Unlike HRSC2016, the annotation boxes in DOTA-v1.0 are quadrilaterals defined by four points \(\{x_{1},y_{1};x_{2},y_{2};x_{3},y_{3};x_{4},y_{4}\}\). DOTA-v1.5 [33], an extension of DOTA-v1.0, increases the number of bounding boxes to 402,089. It includes additional annotations for small objects (around 10 pixels or less) that were missed in DOTA-v1.0 and introduces a new category, container carne (CC), making it a more challenging dataset. In addition to the challenges faced by the HRSC2016 dataset, DOTA presents additional difficulties, including (1) a wide range of image pixel sizes, from \(800\times 800\) to \(4000\times 4000\), making it challenging to train on GPUs such as GTX 3090 ti, and (2) small object sizes, with 98% of the objects in the dataset being smaller than 300 pixels, and 57% smaller than 50 pixels [4], resulting in significant scale differences between tiny and large objects, thereby complicating detection. It is worth noting that the DOTA dataset does not release annotations for the test set; instead, researchers are required to upload their detection results for evaluation, which limits comprehensive analysis of experimental results.