Aquatic Object Detection Through Stereo and Depth Sensor Fusion
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Solution Overview
Problem
Object detection in aquatic environments is challenging due to the dynamic nature of these environments and the presence of moving objects like marine life and debris, which creates noisy data and complicates image processing.
Innovation Solution
An object detection system that combines 2D images, stereo images, and depth data from a position scanner to identify and classify objects. This system uses sensor fusion to improve object detection accuracy by providing additional context through depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection is performed using only 2D images in aquatic environments, then the system complexity is low, but the detection accuracy and ability to distinguish foreground from background objects deteriorates
Solution Approach 1:
The patent combines multiple sensors (2D camera, stereo camera, and position scanner) to create a fused data system. The controller integrates 2D images, stereo images, and depth data to improve object detection accuracy by providing additional contextual information, particularly depth perception, which helps distinguish foreground objects from background elements in noisy aquatic environments.
Solution Approach 2:
The patent transitions from 2D image processing to 3D spatial understanding by incorporating depth data from position scanners and stereo images. This adds the depth dimension to object detection, enabling the system to differentiate objects based on their spatial position and distance, significantly improving detection accuracy in complex aquatic scenes.
2Measurement precision
If sensor fusion is used to combine 2D images, stereo images, and depth data, then object identification accuracy improves, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary processing of sensor data by pre-calculating depth information from stereo images and position scanner data. By preparing and organizing the fused sensor data in advance, the system reduces the computational burden during actual object detection, thereby minimizing processing time while maintaining high identification accuracy.
3Adaptability or versatility
If multiple sensors are integrated to provide additional context, then the ability to detect objects in changing environments improves, but the device complexity and cost increase
Solution Approach 1:
The controller is designed as a multi-functional processing unit that can handle various sensor inputs (2D images, stereo images, position data) and perform multiple operations (depth calculation, object detection, classification). This universal controller reduces overall system complexity by consolidating processing functions rather than requiring separate dedicated processors for each sensor type.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves improved object identification and classification in aquatic environments by distinguishing between foreground and background objects using depth perception, leading to more accurate detection and classification of objects.
Implementation Method 1
determine the depth data based on the time of flight of the reflected signal
Data Source
AI summary
A system and a method for an object detection system for detecting one or more objects in an aquatic environment includes a first image capture device; a second image capture device; a position scanner; a controller in electronic communication with the first image capture device, the second image capture device and the position scanner, the controller configured to receive a 2D image of a scene including the one or more objects; receive or resolve a stereo image of the scene; receive position information of the one or more objects in the scene; determine a depth data of the one or more objects in the scene based on the position information; detect one or more objects in the scene based on the 2D image, the stereo image, and the depth data of the one or more objects.