Ship Safety Video Control for Fog and Rain Event Detection
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Solution Overview
Problem
Conventional ship control systems struggle to efficiently detect and respond to accidents on ships due to limited real-time information on crew and operational states, particularly in challenging conditions like sea fog and rain, leading to potential increased damage from delayed responses.
Innovation Solution
A ship safety control system that processes video data to determine sea conditions, designates control areas, and detects objects and events using neural networks, enabling immediate response measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional surveillance systems are used to detect accidents on ships, then the system structure is simple, but the response time is delayed and detection precision is insufficient in challenging sea conditions
Solution Approach 1:
The system performs preliminary actions by pre-processing video data to enhance images before actual accident detection occurs. This includes applying deblur filters, dehaze filters, and denoise filters in advance to improve image quality, enabling faster and more accurate detection when accidents actually occur.
Solution Approach 2:
The system introduces intermediary processing components between the imaging device and the detection algorithm. These intermediaries include various filter processing units (deblur, dehaze, denoise) that mediate the transformation of raw video data into enhanced video data, improving both detection precision and response time.
2Measurement precision
If video processing is performed without considering sea conditions, then the processing speed is fast, but the detection accuracy deteriorates in fog, rain, or nighttime conditions
Solution Approach 1:
The system applies local quality enhancement by selecting and applying different processing filters based on the specific sea condition detected. Instead of applying a uniform processing method to all videos, the system analyzes video features to determine the sea condition and then applies the appropriate filter (deblur for motion blur, dehaze for fog, denoise for rain), thereby improving detection accuracy while managing processing complexity through conditional specialization.
Solution Approach 2:
The system changes processing parameters dynamically based on sea conditions. By detecting video features such as brightness, contrast, and motion patterns, the system adjusts processing parameters to apply appropriate enhancement levels, transforming the processing approach from static to adaptive, thereby improving accuracy without excessive complexity.
3Reliability
If real-time video processing is implemented across the entire video feed, then the detection coverage is complete, but the processing time increases and response speed decreases
Solution Approach 1:
The system segments the video processing task by dividing the video data into frames and processing them sequentially through different filter stages. This segmentation allows the system to maintain complete detection coverage while managing processing speed through efficient frame-by-frame processing rather than treating the entire video as a single unit.
Solution Approach 2:
The system applies partial processing action by selectively applying different filters based on detected sea conditions rather than applying all possible filters to every frame. This approach ensures adequate detection coverage by applying the necessary processing level for each condition while avoiding the excessive processing time that would result from applying all filters universally.
Data Source
AI summary
The present disclosure relates to a ship safety control method and ship safety control system. According to an embodiment of the present disclosure, the ship safety control method may include determining a sea condition in an area, in which a ship is located, based on a video feature of a first video of a ship situation, preprocessing the first video based on the sea condition, designating a control area in a second video based on the second video generated through the preprocessing, and detecting at least one of an object and occurrence of an event within the control area.