Camera Difference Imaging for Work Machine Object Detection Zones
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Solution Overview
Problem
Existing object detection systems in work machines often misidentify implements as humans due to their movement, leading to unnecessary avoidance actions and work delays.
Innovation Solution
An object detection calibration system that uses a camera and controller to generate a difference image depicting a region of difference between two images, where the region of difference is an exclusion or inclusion zone for object detection, thereby refining the detection process to exclude or include specific areas based on implement movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection is performed on all detected objects including machine implements, then the system may identify potential hazards, but the implement may be misidentified as a human causing unnecessary avoidance actions and work delays
Solution Approach 1:
The image processing system segments the detection area into multiple zones: an exclusion zone containing the machine implement where detection is suppressed, and an inclusion zone where normal object detection occurs. This spatial segmentation allows the system to distinguish between the implement (excluded from detection) and actual objects of interest (included in detection), thereby improving detection accuracy while preventing false alarms that would reduce productivity
Solution Approach 2:
A difference image is generated as an intermediary element by comparing the current image with a reference image. This difference image highlights regions where the implement has moved or where new objects have appeared, allowing the system to focus detection efforts on relevant areas while automatically excluding the implement itself, thus resolving the contradiction between comprehensive detection and avoiding false positives
2Reliability
If the object detection system continuously monitors the entire field of view including the implement, then safety coverage is maximized, but false positive detections increase leading to unnecessary machine downtime
Solution Approach 1:
The monitoring field is segmented into an exclusion zone (containing the implement) and an inclusion zone (containing the working environment). By suppressing object detection specifically in the exclusion zone while maintaining it in the inclusion zone, the system achieves comprehensive safety monitoring of the work area without the false positives that would cause unnecessary machine downtime
Solution Approach 2:
Different detection qualities are applied to different spatial regions: in the exclusion zone, detection is suppressed to avoid false positives from the implement; in the inclusion zone, full detection sensitivity is maintained to ensure safety. This local differentiation of detection quality allows the system to maintain high reliability for actual safety monitoring while eliminating time-loss events caused by false alarms
Data Source
AI summary
A controller may obtain, from a camera, a first image and a second image depicting a work machine in an environment, where an implement of the work machine has moved, or the work machine has moved in the environment, in a depiction of the second image relative to the first image. The controller may generate a difference image that depicts a region of difference between the first image and the second image. The controller may perform object detection on image data from the camera based on the difference image, where the region of difference depicted in the difference image is an exclusion zone or an inclusion zone used for the object detection.