Container Seal Detection Using Multi-Camera Computer Vision
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Solution Overview
Problem
Current methods for verifying the presence and intactness of container seals in the shipping industry are manual and prone to errors, particularly due to glare issues when sunlight affects camera visibility, and lack automation in detecting seal orientation and false positives in motion detection.
Innovation Solution
A system utilizing computer vision and neural networks with multiple cameras, including a pre-processing module for motion detection, a lock detection module, and a seal classification module, which uses attention maps and DeepSort tracking to automate the survey process, reduce glare through wide dynamic range cameras or caps, and accurately detect seal presence, orientation, and intactness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual survey methods are used to verify container seals, then operators can visually inspect seals, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated computer vision system using cameras and neural networks. The system captures images of container seals and uses deep learning models to automatically detect seal presence, orientation, and integrity, eliminating human visual inspection while improving both speed and accuracy.
Solution Approach 2:
The system creates visual copies (images) of the actual seals through multiple cameras positioned at different angles. These image copies are then processed by neural networks to extract seal information, allowing automated verification without physically touching or disturbing the actual seals.
2Illumination intensity
If standard cameras are used to capture seal images, then the system is simple and cost-effective, but glare from sunlight directly affecting the camera lens obscures seal visibility
Solution Approach 1:
The system divides the lighting challenge into multiple segments by using multiple cameras positioned at different angles and heights. Each camera captures the seal from a different perspective, and the neural network processes multiple images to select the best view, effectively segmenting the harmful glare effect across multiple capture points.
Solution Approach 2:
The patent adds spatial dimensions to the image capture system by positioning cameras at various heights and angles (front, rear, side views). This multi-dimensional approach ensures that at least one camera captures the seal without direct sunlight interference, transforming a two-dimensional glare problem into a three-dimensional solution.
3Adaptability or versatility
If the system processes multiple camera feeds simultaneously to detect seals from various orientations, then detection coverage is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing camera feeds to detect motion and identify when a vehicle is present. It saves only the relevant frames when vehicles are detected, rather than processing all captured footage. This preliminary filtering reduces the computational burden before the main seal detection algorithm runs.
Solution Approach 2:
The patent extracts only the essential information from multiple camera feeds using neural networks. Instead of analyzing all pixels from all cameras equally, the system uses attention mechanisms to extract and focus on the most relevant features (seal presence, orientation, integrity) from the multiple images, reducing computational complexity while maintaining detection accuracy.
4Reliability
If motion detection is used to trigger seal verification, then the system activates only when needed, but false positives from non-vehicle motion reduce detection accuracy
Solution Approach 1:
The system uses feedback mechanisms where the neural network continuously refines its motion detection by learning from multiple frames and comparing sequential images. When motion is detected, the system triggers seal verification, and the results feed back into the system to improve future detection accuracy, reducing false positives over time through iterative learning.
Data Source
AI summary
Exemplary embodiments of present disclosure directed towards motion detection module configured to receive third camera feed to detect motion of vehicle. Motion detection module configured to compare selected region of interest from consecutive frames of third camera to detect motion using frame difference. Pre-processing module configured to save consecutive frames from first and second camera when vehicle starts crossing third camera. Lock detection module configured to receive saved frames and detects locks present in saved frames of first and second camera. Seal classification module configured to receive lock images from lock detection module and classifies lock images to identify whether locks are sealed, seal classification module configured to determine seal intactness, color of seals using attention maps and computer vision methods, seal information is passed to post-processing module and is configured to track each seal separately thereby generating final output by considering averaged result over lock images.