Two-Stage Object Detection for Small Bounding Box Accuracy
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Solution Overview
Problem
Conventional object detection systems struggle to achieve fast and accurate detections on small objects in images, often failing to properly identify objects and their features due to insufficient processing and resolution.
Innovation Solution
A two-stage object detection method where an original image is resized and processed to generate a first input image, followed by cropping to create a second input image for a second object detector, enhancing detection accuracy and speed by focusing on the object with higher resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the original image is processed at full resolution, then detection accuracy is improved, but processing time increases and throughput decreases
Solution Approach 1:
The patent divides the detection process into two stages: first stage processes the entire image at reduced resolution to locate potential objects, while the second stage processes only the cropped regions containing detected objects at full resolution. This segmentation allows the system to maintain high detection accuracy for small objects while improving overall throughput by avoiding full-resolution processing of the entire image.
2Productivity
If the image size is reduced for faster processing, then throughput is improved, but detection accuracy deteriorates for small objects
Solution Approach 1:
The patent applies different processing qualities to different regions of the image. The first stage uses reduced resolution for the entire image to maintain throughput, while the second stage applies full resolution specifically to cropped regions containing detected objects. This local quality approach ensures that small objects receive the necessary processing detail for accurate detection while maintaining overall system efficiency.
3Device complexity
If conventional single-stage detection is used, then device complexity is reduced, but the ability to detect small objects deteriorates
Solution Approach 1:
The detection system is segmented into two distinct stages with different processing characteristics. The first stage performs coarse detection at reduced resolution to identify potential object locations, while the second stage performs fine detection on cropped regions at full resolution. This segmentation enables the system to detect small objects effectively while keeping each individual stage relatively simple and manageable.
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium for detecting an object in an image, by applying, on a first input image, a first object detector configured to generate a bounding box around the object in the first input image. The implementations further include identifying a first bounding box having a first bounding box size around the object in the first input image and determining that the first bounding box size is less than a threshold size. Additionally, the implementations further include generating a second input image by cropping an area of the original image corresponding to the first bounding box and identifying, by applying a second object detector on the second input image, a second bounding box around the object in the second input image. Additionally, the implementations further include performing an image analysis function on the object using information from the second bounding box.