# A Review on Observer Assistance Systems for Harvested and Protected Fish Species
## 2 Related Research
### Harvest Estimation  
Although there is no study that exactly matches the harvest estimation, similar or representative studies include the crowdsourcing paradigm [10] and the image stitching studies. The crowdsourcing paradigm makes it faster, cheaper, and easier for researchers to gather the data they need for deep learning.  
Crowdsourcing can provide tremendous benefits with vast amounts of data; however, it requires specific strategies to validate the quality of the data collected. Manual data validation is not an option in this case, as it requires a lengthy process and painstaking work on huge amounts of data. To date, most validation strategies for crowdsourcing are used for categorical data [11; 12; 13; 14; 15].  
In particular, validation strategies for crowdsourcing in computer vision can only be found in image classification cases [16] and object detection [17; 18]. Unlike the object counting case, the currently available datasets are not constructed using a crowdsourcing paradigm. For example, the datasets of UCF CC 50 [19] and TRANCOS [20] are created by hand. As a consequence, the data collected for both datasets are not enough to train a deep learning algorithm (50 images for UCF CC 50 and 1244 images for TRANCOS).  
Additionally, with respect to studies aimed at directly weighing fish, the most commonly used approach to weight estimation uses fish length as a predictor. For example, there is a study using a method of estimating this length from the contour line of a fish (_Orechromis niloticus_) and then treating the fish mass as a response variable [21]. Here, the body surface area of the fish (with or without fins) and the height of the fish were measured and tested on 64 images that were not used in the model fitting process, and the fish contours with and without fins reached a high mean absolute percentage error (MAPE) of 5%. A performance model is proposed, which is a similar model [22] using only length as a characteristic variable; the only comparative model achieved an MAPE = 10%, resulting in excellent results in the validation curve model. In the comparison of the contour without a fin and the case with a fin, the MAPE = 12% is highly evaluated, but there seems to be difficulty in setting the parameters. As a result, there is a 2% difference in performance improvement between the two models. Another study reported in [22] used surface area only, with an MAPE = 5-6%, and [23] applied several mathematical models to harvested Asian sea bass to improve performance.  
Here, the mass is measured in grams, and the fish surface area is studied by calculating the units of cm\({}^{2}\) on an image with a size of 1 mm per image pixel. MAPE values are 5.1% and 4.5%, respectively, when fitted to two different fish images of 1072 Asian perch [24]. In general, fitting parameters are studied by applying them differently depending on the species [25; 26].  
The focus of this study was to continue developing a method for automatic estimation of harvested fish weight from images. Several conditions were presented to derive the experimental results. The condition is to assume that a model excluding fins and tails will be more accurate compared to a model using full-fish silhouettes [23]. It is obviously much easier to extract the total fish surface area than to exclude fins that are not precisely defined [22; 27]. Therefore, using the additional formula of using Deep Convolutional Neural Networks (CNN) [23; 28], we present results that achieved an MAPE of 3.6% and an MAPE = 11.2% in length estimation.