Development of an underwater camera system for inland freshwater aquaculture

Computer vision and image processing technologies are applied towards aquatic research to understand fish and its interaction with other fishes and their environment. The understanding of vision-based data acquisition and processing aids in developing predictive frameworks and decision support systems for efficient aquaculture monitoring and management. However, this emerging field is confronted by a lack of high-quality underwater visual data, whether from public or local setups. An accessible underwater camera system that intensively obtains underwater visual data periodically and in real-time is the most desired system for such emerging studies. In this regard, an underwater camera system that captures underwater images from an inland freshwater aquaculture setup was proposed. The components of the underwater camera system are primarily based on Raspberry Pi, an open-source computing platform. The underwater camera continuously provides a real-time video streaming link of underwater scenes, and the local processor periodically acquires and stores data from this link in the form of images. These data are stored locally and remotely. Also, the local processor initiates a connection to a remote processor to allow the remote view of the real-time video streaming link. Aside from accessing the data and streaming link remotely, the remote processor analyzes the statistics of the underwater images to motivate the application of color balance and fusion, a state-of-the-art underwater image enhancement method. The applications of the proposed system and the enhancement to the captures are objectively evaluated. The proposed system captured around 1.2 Gb worth of 8 MP underwater images during daytime every day and stored these images in cloud storage. Also, the system captured subjects within 10-35 cm of turbid fishpond water. The statistical analysis of the gathered data revealed that underwater images from turbid fishpond setups have low quality in terms of inaccurate color representations (i.e., dominant green intensities and mostly submissive blue intensities) and low contrast. These observations appropriated the application of color balance and fusion to the locally acquired data. Furthermore, the objective evaluation revealed that color balance and fusion is the most effective method of improving information content and edge details, as quantified by high color information entropies and high average gradients. These metrics revealed the effectiveness of the proposed data acquisition and preprocessing system

Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

An image classifier for underwater fish detection using classification tree-artificial neural network hybrid

Fish detection using imaging technologies and computer vision systems is considered as an effective tool in fish monitoring for increasing the production to satisfy future global demands. This persistent tasks, with image classification as one of its subtasks, encounters challenges due to the complex nature of underwater images. A proposed approach to address this subtask was to create a hybrid image classification model from classification tree and artificial neural network. The classification tree component performed feature selection to extract a reduced representation of the fundamental dataset, derived from a series of acquired and processed underwater images in a land-based aquaculture setup. This said representation was then fed to a feedforward artificial neural network to develop such model. The best configuration of this hybrid model was determined, based on learning time and cross entropy, and was compared to a classification tree and an artificial neural network, both developed from the fundamental dataset, based on training and testing accuracies. The best performing hybrid model, composed of 100 hidden neurons in the artificial neural network component, achieved training and testing accuracies of 93.6% and 78.0%, respectively, hence, providing a competitive solution to the image classification in fish detection problem. © 2020 IEEE