Sighting Human Detection to Trigger Abnormal Aiming Warnings
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Solution Overview
Problem
Existing sighting devices prioritize shooting accuracy but neglect safety, potentially leading to accidental harm to humans in low-light or unforeseen circumstances, limiting their use to civilian applications and preventing military use.
Innovation Solution
A human detection and warning method integrated into sighting devices that recognize human targets within the field of view and trigger an abnormal aiming mode to prevent shooting, using deep learning-based target recognition and various warning mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human detection and warning functions are added to sighting devices, then safety is improved, but device complexity increases
Solution Approach 1:
The patent combines human detection, target recognition, and warning functions into an integrated sighting device system. The processor merges image processing, human detection algorithm execution, and warning signal generation into a unified control architecture, allowing multiple functions to share hardware resources and reduce overall system complexity despite the added capabilities.
Solution Approach 2:
The sighting device is designed with multi-functionality, serving both traditional aiming functions and new human detection/warning functions. The same image sensor, processor, and display system are utilized for both target acquisition and safety monitoring, making the device versatile without requiring entirely separate systems for each function.
2Measurement precision
If deep learning-based target recognition is implemented, then target recognition accuracy is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary action by pre-processing images to identify potential target regions before applying the computationally intensive deep learning human detection algorithm. By first detecting general target areas and then focusing computational resources only on those specific regions, the system maintains high recognition accuracy while significantly reducing overall energy consumption compared to full-image deep learning processing.
3Reliability
If human detection algorithm is added to process images in real-time, then safety warning capability is improved, but productivity decreases
Solution Approach 1:
The image processing workflow is segmented into multiple stages: initial target region detection, human detection algorithm execution on identified regions, and warning signal generation. This segmentation allows the system to process only relevant image portions through the computationally intensive human detection algorithm, maintaining real-time safety warning capability while improving overall processing efficiency and reducing bottlenecks.
Data Source
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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.