# A Review on Observer Assistance Systems for Harvested and Protected Fish Species
## 2 Related Research
### Electric Monitoring System  
Electronic monitoring (EM) began to be installed on ships for the purpose of preventing safety accidents, but it has recently been used as a tool to collect information on the fishing process and the numbers of fish caught [44; 45; 46; 47]. However, there is no direct, real-time analysis in these systems; they are manually screened and analyzed in marine data centers. This enables the recording of harvested fish statistics; however, manual screening of EMS images and videos is time consuming and labor intensive [46; 48].  
Therefore, an effective method is to use conventional image processing and machine learning techniques to automatically perform the aforementioned tasks. In this regard, a study [49] was conducted using a conveyor-belt-based machine vision system to detect fish and determine their length and species. In addition, a further study identified three types of fish [50] from images acquired under controlled lighting using manually annotated shape and texture features and linear discriminant analysis.  
Subsequent research [51] has used image processing techniques to evaluate the BL of fish in images acquired with a white background, and [52] used machine vision techniques to track fish and study their counts in laboratory-acquired underwater images. In [53], moving averages and feature-matching algorithms were used to detect, track, and count fish in coral reef videos collected with high-contrast backgrounds. A study has also been conducted to detect feeder fish in static containers using image processing techniques [54].  
Recently, Convolutional Neural Networks (CNNs) have been applied to solve complex machine vision problems [55; 56; 57; 58]. CNNs utilize spatial convolution operations in networks. Once trained, CNNs can extract features from input images with few pre-processing requirements [59] and can achieve remarkable performance in machine vision tasks. Perhaps we could use a CNN approach to solve the fish detection and recognition problem.  
In [60], CNN and the nearest-neighbor algorithm were used to detect and count discarded harvested fish in fishing trawler videos. The study [61] used a faster Region-based Convolutional Neural Network (R-CNN) [62] to detect fish and identify fish species in underwater videos. In another study [63], fish were recognized in underwater videos through a CNN matrix decomposition and support vector machine. In [64], three fish recognition tasks were completed in coral reef videos using a CNN with single-shot multibox detector architecture. There is also a study using CNNs with VGG architecture to identify eight common species and types of harvested tuna and billfish [65].  
Research in papers [66; 67] succeeded in detecting fish in unconstrained underwater videos using a CNN with a one-time architecture [68]. Studies in [69] used a two-step graph-based approach and a CNN model to track the movements of several fish in a stream in an underwater video. Further research in [70] also used a region-based modeling approach and deep CNN to detect fish from images acquired from ships and to recognize species of fish. In [71], we measured fish BL using a CNN on images acquired from ships. Another paper [72] detected fish images and estimated the length of the fish using three R-CNNs.  
However, it is true that EMS images or videos acquired from ships have many problems, such as image quality and obscuration of angles due to moisture; consequently, accurate recognition is still a challenge.