Sighting Human Detection to Prevent Accidental Aiming
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Solution Overview
Problem
Existing sighting devices prioritize shooting accuracy but neglect safety, particularly in low-light conditions, risking accidental harm to humans.
Innovation Solution
A human detection and warning method integrated into sighting devices that recognize human targets and trigger an abnormal aiming mode to prevent accidental shooting, using deep learning-based target recognition and various warning mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If sighting devices are designed to improve shooting accuracy, then shooting precision is improved, but safety against accidental human targeting is worsened
Solution Approach 1:
The system performs preliminary human target detection and recognition before allowing shooting to proceed. The deep learning model analyzes the scene image in advance to identify human targets, and the abnormal aiming mode is activated preemptively when a human is detected, preventing accidental shooting before it can occur.
Solution Approach 2:
The system continuously monitors the scene image and providing real-time feedback about human target detection to the shooting control system. When the deep learning model detects a human target, it sends feedback signals to trigger the abnormal aiming mode, which then provides visual and auditory warnings to the operator.
2Reliability
If human detection and warning function is added to sighting devices, then safety is improved, but device complexity increases
Solution Approach 1:
The sighting device is enhanced with multi-functionality by integrating both the original shooting assistance function and the new human detection safety function into a single system. The deep learning model serves dual purposes: recognizing legitimate targets for shooting while simultaneously detecting human targets to trigger safety warnings, eliminating the need for separate detection systems.
Solution Approach 2:
The deep learning-based target recognition model acts as an intermediary between the scene image capture and the shooting control decision. It processes the scene image to identify both legitimate targets and human targets, mediating the information flow to trigger appropriate responses (shooting permission or safety warnings) without requiring direct complex interactions between all system components.
3Measurement precision
If deep learning-based target recognition is used, then human detection accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by focusing the deep learning model's computational resources on specific regions of interest within the scene image rather than processing the entire image uniformly. The model prioritizes areas where human targets are likely to appear, reducing unnecessary computational overhead while maintaining high detection accuracy.
Solution Approach 2:
The system performs preliminary image processing and feature extraction before the deep learning model analysis. By pre-processing the scene image to enhance contrast, segment regions, and identify potential target areas, the system reduces the computational burden on the deep learning model during the critical human detection phase, thereby decreasing overall processing time.
Data Source
AI summary
A human detection and warning method includes: acquiring a current scene image within a current field of view of a sighting device; determining whether there is a human target being currently aimed at and/or whether there is currently target tracking of a human target, based on a target recognition result of the current scene image and a position of an aiming mark in the current scene image; and triggering the sighting device to enter an abnormal aiming mode if there is a human target being currently aimed at and/or there is currently target tracking of a human target.