Helmet Detection via Deformable Convolution and Head Matching
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Solution Overview
Problem
The reliance on monitoring staffs to observe construction video images for helmet safety leads to wasted manpower and missed detections, resulting in poor monitoring effectiveness.
Innovation Solution
A method and apparatus using a camera device and an electronic device to acquire, process, and analyze images for detecting safety helmet wear, employing deformable convolution processing and matching degree analysis to accurately identify helmet presence and send alarms, thereby automating the monitoring process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monitoring staff manually observe construction video images to determine helmet wearing status, then the detection can be performed, but manpower is wasted and detection accuracy decreases due to human error and missed observations
Solution Approach 1:
The patent replaces the mechanical system of manual observation by monitoring staff with an automated image processing system using deep learning models. The system automatically detects human bodies, extracts head images, identifies helmets, and determines wearing status without human intervention, thereby eliminating manpower waste and improving detection accuracy through consistent automated analysis
Solution Approach 2:
The system enables self-service detection where the video images automatically analyze themselves for helmet wearing status. The deep learning model performs autonomous detection, classification, and judgment without requiring external human observers, making the monitoring process self-sufficient and eliminating the need for continuous manual supervision
2Reliability
If monitoring staff manually observe construction video images, then detection is performed, but some images are missed resulting in poor monitoring effectiveness
Solution Approach 1:
The automated system enables continuous monitoring of construction video images without interruption. Unlike manual observation which has gaps and fatigue limitations, the deep learning model processes images continuously and consistently, ensuring no detection gaps and maintaining constant monitoring effectiveness over extended periods
Solution Approach 2:
The system replaces the limited human capacity for continuous observation with an automated computational system that can process images without fatigue, distraction, or attention lapses, thereby eliminating missed detections and ensuring reliable continuous monitoring coverage
3Productivity
If automated image processing is used to detect helmet wearing, then manpower is saved and detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex monitoring task into distinct modular processing stages: human body detection, head image extraction, helmet identification, and wearing status determination. Each stage is handled by specialized computational modules, making the overall complex system manageable through functional decomposition and enabling parallel processing improvements
Solution Approach 2:
The deep learning model serves multiple functions within a single unified system: it detects human bodies, extracts head regions, identifies helmet presence, and determines wearing status. This multi-functionality reduces the need for separate specialized systems for each detection task, thereby managing complexity while maintaining high productivity
Data Source
AI summary
The present application discloses a method and an apparatus for detecting wearing of a safety helmet, a device and a storage medium. The method for detecting wearing of a safety helmet includes: acquiring a first image collected by a camera device, where the first image includes at least one human body image; determining the at least one human body image and at least one head image in the first image; determining a human body image corresponding to each head image in the at least one human body image according to an area where the at least one human body image is located and an area where the at least one head image is located; and processing the human body image corresponding to the at least one head image according to a type of the at least one head image.