Sensor-Based Aquatic Object Detection with Deep Learning Tracking
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Solution Overview
Problem
Conventional techniques for detecting drowning in aquatic environments are inaccurate, expensive, and difficult to deploy, often failing to distinguish between fixed objects and transient bodies, and are unable to consistently identify and classify in-water objects, leading to low drowning detection rates.
Innovation Solution
A system utilizing deep learning modeling and sensor-based data analysis, including a GPU for object detection, a CPU for tracking, and a classifier to distinguish between foreground and background data, accurately identifies and tracks individuals in aquatic environments, triggering alarms when a submerged person is detected for an extended period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor-based techniques are used for drowning detection, then the system can detect objects in water, but it fails to distinguish between fixed objects and transient bodies, resulting in low detection accuracy
Solution Approach 1:
The system dynamically adapts by using deep learning models that learn to distinguish between fixed and transient objects through motion patterns and temporal behavior analysis, rather than relying on static sensor thresholds
Solution Approach 2:
The system changes detection parameters dynamically by adjusting classification criteria based on motion characteristics, depth, and temporal patterns to accurately differentiate between fixed objects and transient bodies
2Reliability
If conventional drowning detection systems are deployed, then they can monitor aquatic environments, but they are expensive and difficult to install, especially for retrofitting existing pools
Solution Approach 1:
The system uses multi-functional sensors that can detect both fixed objects and transient bodies, and serve multiple purposes including drowning detection, object classification, and motion tracking, reducing the need for specialized equipment
Solution Approach 2:
The system uses software-based deep learning models that can be deployed on existing hardware platforms, creating a virtual intelligence layer that copies advanced detection capabilities without requiring expensive specialized physical equipment
3Productivity
If conventional sensor systems are used, then they can process input data, but the input is not processed accurately enough to consistently identify and classify in-water objects
Solution Approach 1:
The system replaces conventional mechanical signal processing with deep learning-based intelligent processing, using neural networks to automatically learn and extract features from sensor data for accurate object identification and classification
Solution Approach 2:
The system implements feedback loops where detection results are continuously refined through deep learning model updates and adaptive processing, improving consistency in object identification over time
Data Source
AI summary
Techniques for analysis and deep learning modeling of sensor-based object detection data in bounded aquatic environments are described, including capturing an image from a sensor disposed substantially above a waterline, the sensor being housed in a structure electrically coupled to a light housing, converting the image into data, the data being digitally encoded, evaluating the data to separate background data from foreground data, generating tracking data from the data after the background data is removed, the tracking data being evaluated to determine whether a head or a body are detected by comparing the tracking data to classifier data, tracking the head or the body relative to the waterline if the head or the body are detected in the tracking data, and determining a state associated with the head or the body.