Adaptive Obstacle Detection for Work Vehicles Using Variable Image Timing
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Solution Overview
Problem
Existing obstacle detection systems in work vehicles perform arithmetic processing on all captured images regardless of the presence of obstacles, leading to inefficient heat generation and high power consumption due to unnecessary processing of images without obstacles.
Innovation Solution
An obstacle detection system that adjusts the frequency of image transmission to the obstacle detection unit based on obstacle positional information, prioritizing images with obstacles for higher frequency processing and using a learning-type obstacle detection unit trained with deep learning to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If obstacle detection processing is performed on all captured images at high frequency, then detection accuracy and reliability are improved, but power consumption and computational burden increase significantly
Solution Approach 1:
The patent applies dynamics by making the image processing frequency adaptive rather than fixed. The obstacle detection unit dynamically adjusts its processing frequency based on the presence of obstacles detected by the radar unit. When obstacles are detected, the processing frequency increases to improve detection reliability; when no obstacles are present, the frequency decreases to reduce power consumption. This dynamic adjustment resolves the contradiction between maintaining high detection reliability and reducing energy consumption.
Solution Approach 2:
The patent changes the parameter of processing frequency based on obstacle presence. By monitoring whether obstacles are detected by the radar unit and adjusting the image processing frequency accordingly, the system optimizes the balance between detection reliability and power consumption. This parameter change allows the system to operate efficiently under different conditions.
2Reliability
If obstacle detection processing is performed on all captured images, then comprehensive detection coverage is achieved, but processing efficiency decreases due to unnecessary processing of empty images
Solution Approach 1:
The patent applies partial action by selectively processing only those captured images where obstacles are detected by the radar unit, rather than processing all images. This partial processing approach maintains comprehensive detection coverage for areas where obstacles exist while avoiding unnecessary processing of empty images, thereby improving overall processing efficiency without sacrificing detection reliability in critical areas.
Solution Approach 2:
The patent segments the image processing task based on obstacle presence information from the radar unit. The system divides the overall processing workload into two segments: full processing when obstacles are detected and minimal processing when no obstacles are present. This segmentation allows the system to maintain high detection coverage where needed while improving efficiency by reducing processing of unnecessary images.
3Measurement precision
If image processing frequency is increased for captured images containing obstacles, then detection accuracy improves, but system responsiveness to non-obstacle areas decreases
Solution Approach 1:
The patent applies dynamics by making the processing frequency responsive to real-time obstacle detection status. The system dynamically switches between high-frequency processing mode (when obstacles are detected) and low-frequency processing mode (when no obstacles are present). This dynamic response ensures high detection accuracy for obstacles while maintaining adequate system responsiveness to changes in the environment, resolving the contradiction between precision and speed.
Data Source
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AI summary
Provided is an obstacle detection system for a work vehicle which obstacle detection system can efficiently perform an arithmetic processing operation. The obstacle detection system includes: an image capturing section (3) configured to generate and output a captured image of an obstacle detection target area; an obstacle positional information acquisition section (51) configured to acquire obstacle positional information on an obstacle in the obstacle detection target area; a learning-type obstacle detection unit (6) trained to output obstacle detection information including a detection result of the obstacle based on an input image; and an image transfer processing section (5) configured to transmit the input image based on the captured image to the obstacle detection unit (6) at a predetermined time interval. The image transfer processing section (5) includes a time interval change section (55) configured to change the time interval based on the obstacle positional information.