Image Processing System for Conditional Animal Detection
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Solution Overview
Problem
Existing image processing systems face a significant increase in processing load and resource usage when detecting animals across multiple locations, due to the large number of captured images that need to be analyzed.
Innovation Solution
An image processing system that calculates traffic volume of people and vehicles from captured road images, estimates the necessity degree for animal detection processing based on this data, determines whether to execute animal detection processing, and only proceeds with the processing when deemed necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If animal detection processing is executed on all captured images from multiple locations, then detection coverage is improved, but processing load and resource usage increase
Solution Approach 1:
The system performs preliminary traffic volume calculation on captured images before executing animal detection processing. By calculating traffic volume first and using it as a prerequisite condition, the system determines whether animal detection should be executed, thereby avoiding unnecessary processing and reducing overall processing load while maintaining detection coverage where needed
Solution Approach 2:
Instead of executing animal detection processing on all captured images, the system applies partial action by selectively processing only those images where traffic volume indicates potential animal presence. This partial processing approach reduces resource consumption while maintaining adequate detection coverage in high-risk areas
2Reliability
If animal detection processing is executed on all captured images, then detection accuracy is improved, but resource usage increases
Solution Approach 1:
The system performs preliminary traffic volume calculation before animal detection to identify images warranting further processing. This preliminary filtering step reduces the number of images requiring resource-intensive animal detection processing, thereby lowering overall resource usage while maintaining detection accuracy for relevant cases
Solution Approach 2:
The system changes the processing parameter by using traffic volume as a conditional threshold. Images are processed through animal detection only when traffic volume exceeds certain thresholds or patterns suggesting animal presence, thereby optimizing resource allocation while preserving detection accuracy for high-probability cases
3Reliability
If animal detection processing is executed on all captured images, then detection completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary traffic volume calculation on captured images before executing animal detection processing. By establishing traffic volume as a prerequisite condition, the system filters out images unlikely to contain animals, thereby reducing total processing time while maintaining detection completeness for relevant cases
Solution Approach 2:
The system implements periodic filtering by first calculating traffic volume and then conditionally executing animal detection based on traffic patterns. This two-stage periodic processing approach reduces overall processing time by skipping animal detection on images where traffic conditions indicate low animal presence probability
Data Source
AI summary
The image processing system includes: a traffic volume calculation means for calculating at least one of a traffic volume of people and a traffic volume of vehicles by using a captured image of a road; a necessity degree estimation means for estimating a necessity degree that indicates a degree of necessity for executing animal detection processing of detecting an animal by using the captured image, based on the calculated traffic volume; an execution determination means for determining whether to execute the animal detection processing using the captured image, based on the estimated necessity degree; and an animal detection processing means for, when it is determined that the animal detection processing is to be executed, executing the animal detection processing using the captured image.