# Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision
## 1 Introduction  
The tilapia (_Oreochromis_ sp.) has characteristics such as fast growth, easy cultivation, and rich nutritional content [1]. Accurate measurement of fish length information is crucial for estimating fish mass, bait control, and aquaculture sales [2]. Length and body weight are fundamental biological features of fish populations and can reflect individual physiological states [3]. Fish mass information is essential for various aquaculture management activities. Traditional fish mass measurement methods require capturing fish and measuring their length and weight, which can result in fish mortality, resource waste, and poor measurement consistency [4]. With the continuous development of computer vision technology, more and more researchers are investing in the field of underwater binocular vision. Non-contact fish mass estimation methods [5] have attracted increasing attention from researchers. [PERSON] et al. [6] used deep learning techniques, specifically the YOLOv4 model, to detect and analyze fish head, body, and tail features. Completely visible fish (CVF) were identified by the segmentation analysis technique. The fish biomass was calculated from the estimated length using a length-mass relationship calibration curve. The method showed high accuracy and reliability in estimating fish biomass, especially in cryptic environments. [PERSON] et al. [7] collected about 2500 images of golden eye bass and estimated their mass. Two instances of a convolutional neural network (CNN) were trained using LinkNet-34 architecture. A CNN that regresses the weight directly from the images was also trained to estimate the mass of the fish efficiently and automatically. That study demonstrated thepotential for automated mass estimation of golden eye bass using deep learning techniques. [PERSON] et al. [8] designed the CVS to be able to efficiently distinguish the fish body from the background and fins, and the extracted fish body regions could be successfully used to predict fish weight and carcass weight. That study demonstrated the potential of utilizing computer vision and deep learning techniques for fast, accurate, and non-invasive measurements in the aquaculture industry. [PERSON] et al. [9] improved the feature pyramid network (FPN) for Mask R-CNN and proposed an effective scheme for the accurate measurement of fish body length and body width in precision agriculture, which can achieve highly accurate measurement results in different contexts. But, in these methods, the fish were caught and placed on a platform. Such a process may cause a lot of stress to the fish, affecting its growth and even leading to its death [10]. Of course, there are researchers who conduct experiments underwater. [PERSON] et al. [11] used a single underwater camera to acquire images in a controlled environment in order to shorten the measurement time and reduce fish stress. The length and mass of the fish were also estimated by image processing and regression analysis techniques. [PERSON] et al. [12] developed an automated system using a Kinect as an RGB-D camera to collect depth map and top view images of 295 farmed bass of different sizes. The geometric features of the back of the fish and machine learning algorithms were utilized to estimate the mass of the fish. [PERSON] et al. 's [13] study proposed an unsupervised fin removal method. The fish mass was estimated based on the area and the area squared after fully automated caudal fin removal with a maximum relative error of 8.46%. Although this method of mass prediction based on fish body area is efficient, it is not possible to accurately estimate the mass of the fish when its body plane is at an angle to the camera level.