Monocular Underwater Camera Biomass Estimation Without Depth Sensors
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Solution Overview
Problem
Existing methods for estimating biomass of aquatic livestock, such as farmed fish, are time-intensive and potentially harmful, and rely on costly and unreliable depth-sensing hardware like stereo cameras and ToF sensors, which are prone to environmental interference and maintenance issues.
Innovation Solution
Utilizing a monocular underwater camera system with computer vision and machine learning techniques to process images and estimate biomass through 2D truss networks, eliminating the need for depth-sensing hardware and providing accurate biomass distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereo cameras or ToF sensors are used for depth sensing, then biomass estimation accuracy can be improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and removes the depth-sensing hardware component from the system. Instead of using stereo cameras or ToF sensors to capture depth information, the invention processes standard 2D images from a single camera to estimate biomass, thereby eliminating complex depth-sensing hardware while maintaining estimation capability
Solution Approach 2:
The patent creates a computational model that copies the depth estimation function normally performed by hardware. The machine learning model trained on 2D images replicates the biomass estimation capability that would traditionally require depth-sensing hardware, providing a software-based alternative to physical depth sensors
2Measurement precision
If stereo cameras are used for depth sensing, then biomass estimation can be achieved, but reliability decreases due to environmental interference and maintenance issues
Solution Approach 1:
The patent replaces expensive, maintenance-prone depth-sensing hardware with a simpler, more reliable image processing approach. Standard cameras are used instead of specialized depth sensors, and the solution accepts that image quality may vary but maintains robustness through algorithmic processing rather than hardware precision
3Measurement precision
If manual fish removal and weighing is performed, then biomass data can be obtained, but time consumption increases and fish may be harmed
Solution Approach 1:
The patent replaces the mechanical process of manually removing and weighing fish with an optical-mechanical system. Images are captured from the water and processed computationally to estimate biomass, substituting physical handling with remote sensing and algorithmic analysis
Solution Approach 2:
The patent introduces an intermediary computational model between image capture and biomass determination. The machine learning model acts as a mediator that translates visual information into biomass estimates, eliminating the need for direct physical measurement while maintaining accuracy
4Loss of time
If only a small portion of fish population is measured manually, then time consumption is reduced, but measurement precision of population characteristics deteriorates
Solution Approach 1:
The patent creates a universal measurement system that can estimate biomass for all fish in the population simultaneously rather than requiring individual measurements. The single camera captures the entire population, and the processing system universally applies the estimation algorithm to each fish, providing comprehensive population data efficiently
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining a plurality of images of fish captured by a monocular underwater camera; providing the plurality of images that were captured by the monocular underwater camera to a first model trained to detect one or more fish within the plurality of images; generating one or more values for each detected fish as a set of values; generating a biomass distribution of the fish based on the set of values; and determining an action based on the biomass distribution.