Object Detection Layer Selection and Result Integration
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Solution Overview
Problem
Conventional object detection methods face reduced accuracy due to limiting the search layer to the same layer for both first and second detections, and using different models for each detection, which can lead to suboptimal detection results.
Innovation Solution
The method involves generating multiple layer images by scaling the input image, performing initial detection on these images, selecting the most appropriate layer for further detection based on learned data, and integrating the results from both detection steps to enhance accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the search layer is limited to the same layer for both first and second detections, then processing speed is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent dynamically selects the search layer for the second detection based on the detection results of the first detection and learned data, rather than statically limiting to the same layer. This dynamic adaptation allows the system to optimize between speed and accuracy for each specific detection scenario.
Solution Approach 2:
The patent changes the layer parameter for the second detection based on learned data and first detection results. By adjusting which layer to search in the second detection, the system can improve accuracy when needed while maintaining speed when the same layer is appropriate, resolving the contradiction between processing speed and detection accuracy.
2Adaptability or versatility
If different models are used for the first detection and the second detection, then detection coverage is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent uses the detection results from the first detection as feedback to determine the search layer for the second detection. This feedback mechanism ensures that the second detection builds upon the first detection results, improving overall accuracy while maintaining comprehensive coverage through the two-stage approach.
Solution Approach 2:
The first detection serves as a preliminary action that identifies potential target regions. Based on these preliminary results and learned data, the system then performs a more focused second detection, ensuring both broad coverage and high accuracy in the final detection results.
3Measurement precision
If multiple layer images are generated and all are searched, then detection accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The patent extracts and utilizes learned data about which layers are most likely to contain targets. This extracted knowledge is then applied to selectively limit the search to only the most relevant layers, eliminating unnecessary searches in other layers and thus maintaining high accuracy while improving processing speed.
Solution Approach 2:
Instead of performing detection on all generated layer images, the patent performs detection on only the necessary subset of layers determined by learned data and first detection results. This partial action approach avoids excessive processing while still achieving comprehensive and accurate detection.
Data Source
AI summary
An object detection method includes an image acquisition step of acquiring an image including a target object, a layer image generation step of generating a plurality of layer images by one or both of enlarging and reducing the image at a plurality of different scales, a first detection step of detecting a region of at least a part of the target object as a first detected region from each of the layer images, a selection step of selecting at least one of the layer images based on the detected first detected region and learning data learned in advance, a second detection step of detecting a region of at least a part of the target object in the selected layer image as a second detected region, and an integration step of integrating a detection result detected in the first detection step and a detection result detected in the second detection step.