FCN Image Processing for Small Object Detection Accuracy
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Solution Overview
Problem
Existing image processing techniques struggle with detecting small detection targets, such as white canes, due to low detection accuracy when the target occupies a small region of the image and is easily confused with other features.
Innovation Solution
An image processing system utilizing a Fully Convolutional Network (FCN) with convolutional layers that maintain spatial features, performing binarization and expansion processing to enhance detection accuracy, and employing a teacher image-based training method to differentiate between detection targets and background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing or CNN is used for detection, then the system is simple to implement, but detection accuracy for small targets deteriorates
Solution Approach 1:
The detection process is segmented into three distinct modules: region proposal network for candidate region generation, classification network for target identification, and visualization network for reasoning process display. This segmentation allows each module to specialize in specific tasks, improving overall detection accuracy for small targets while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of visualization by adding the visualization network that displays the reasoning process of detection. This goes beyond traditional binary classification by providing intermediate information about detection confidence and region proposals, effectively adding a dimensional layer to the output that enhances interpretability without compromising detection accuracy.
2Measurement precision
If CNN with fully connected layers is used, then processing speed is fast, but classification accuracy for small coexisting subjects deteriorates
Solution Approach 1:
The patent extracts and removes the fully connected layers from the traditional CNN architecture, retaining only the convolutional layers that are better suited for spatial feature extraction. This extraction of problematic components allows the system to maintain fast processing speeds while achieving superior classification accuracy for small targets through the region proposal mechanism.
Solution Approach 2:
The region proposal network acts as an intermediary between image input and final classification. It generates candidate regions that are then fed to the classification network, serving as a mediating step that improves accuracy by focusing computational resources on relevant regions rather than processing the entire image through fully connected layers.
3Reliability
If traditional detection methods are used, then computational resources are conserved, but detection reliability for inconspicuous targets deteriorates
Solution Approach 1:
The region proposal network performs preliminary action by generating and filtering candidate regions before the main classification process. This preliminary step identifies promising areas in the image, allowing the subsequent classification network to focus computational resources only on these regions rather than processing the entire image, thereby improving detection reliability while managing computational consumption.
Data Source
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AI summary
The present invention provides a technique suitable for the detection of small objects such as white canes. In order to solve this problem, one representative example of this image processing system comprises a video acquisition unit configured to input an input image acquired as a video, an estimation unit including a Fully Convolutional Network (FCN) learning model trained with respect to a region of a predetermined detection target, and configured to generate a region estimation image for the detection target by processing the input image with the learning model, and an image processing unit configured to detect the region of the detection target based on the region estimation image.