# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Introduction to DL-Based Methods for Wake Detection
#### 3.3.2 Single-Stage and Two-Stage Detectors  
DL-based object detectors are usually categorized into two-stage and single-stage detectors [57]. The following subsections describe the main differences between the two types of detectors and provide examples regarding the evolution and state-of-the-art of such models.  
The first detection models developed were the two-stage detectors, a categorization which refers to the sequential application of two processes:  
* Region proposal is performed by region proposal networks (RPNs), where the image is scanned for a set number of regions in the image that most likely contain objects of interest. In general, the output on an RPN will be a set number of BB proposals and scores representing the probability of the presence of objects at each location.
* Classification, where the content in each BB is classified as one of the object classes of interest or discarded as background. During this stage, BBs are also adjusted through regression to better fit the detected objects.  
Figure 4: A schematic view of a detection model, divided into backbone, neck, and heads. The backbone module extracts the essential features at different resolutions and at different levels of spatial and semantic values. The neck combines the feature maps in various ways. Finally, multiple head modules produce the finals outputs, from the localization of objects, their classes, or any other information the model is designed to provide.  
The first DL-based detection algorithms were very slow and inefficient, as they used intuitive but computationally inefficient methods of region proposal, such as sliding windows. Nowadays, region proposal has become much more efficient, and two-stage detectors are still used at large scale. Common examples of two-stage detectors include the R-CNN family of networks (e.g., R-CNN, Fast R-CNN) [70; 71].  
Differently from two-stage detectors, single-stage or one-stage detectors use pre-generated candidate regions as region proposals, applying object classification and BB regression directly. When compared to two-stage detectors, this approach sacrifices a certain amount of accuracy in exchange for smaller networks with less parameters and increased processing speed. Representative examples of one-stage detectors are the single shot multibox detector (SSD) [72] and its descendants, and the family of you only look once (YOLO) models [73; 74; 75; 76; 77]. By approaching the detection task as a single-shot regression, YOLO implements a grid-based approach to predict class probabilities, BBs, and the relative confidence scores.  
It is important to note that research in the field of maritime surveillance favors single-stage detectors, which provide better characteristics for real-time or near-real-time applications, due to being lighter and, in general, more efficient. A 2023 survey conducted by [PERSON] et al. [78] on real-time ship detection in SAR imagery showed a clear prevalence of one-stage detectors for this type of application, with architectures based on several versions of YOLO being widely favored in 37 out of the 70 reviewed works. This is consistent with similar works related to DL-based ship detection in optical imagery [79]. However, as shown in the following sections, for wake detection, both one-stage and two-stage detectors are found, with a prevalence of custom architectures developed specifically to tackle this challenge.