Object Detection via Multi-Scale Feature Fusion
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Solution Overview
Problem
Existing object detection technologies face challenges in achieving high processing performance, precision in computation and inference, and generalization capability, particularly in real-time applications with limited computational resources.
Innovation Solution
The proposed object detection method employs a convolutional neural network architecture that integrates lightweight models with network acceleration strategies. This involves extracting initial feature layers from an original image, fusing them through interpolation and addition operations, and inputting the fused layers into detection heads to obtain bounding box and classification probability distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional object detection models are used, then detection accuracy can be achieved, but processing speed and real-time performance deteriorate due to high computational complexity
Solution Approach 1:
The object detection system is segmented into multiple independent components: a backbone network for feature extraction, a neck module for feature fusion, and detection heads for object detection. This segmentation allows each component to be optimized independently and enables parallel processing, significantly improving processing speed while maintaining detection accuracy.
Solution Approach 2:
The system uses a lightweight backbone network that extracts only the necessary feature layers (P2, P3, P4, P5) from the input image, rather than processing all possible features. This partial action approach reduces computational complexity while maintaining sufficient feature representation for accurate object detection at 300 FPS.
2Loss of time
If lightweight models are used to improve processing speed, then inference time is reduced, but detection precision and accuracy deteriorate
Solution Approach 1:
The system merges multiple feature layers (P2, P3, P4, P5) of different resolutions through the neck module using interpolation and addition operations. This fusion combines the strengths of shallow features (fast computation) and deep features (high precision), achieving both fast inference and accurate detection.
Solution Approach 2:
The system operates at multiple resolution dimensions simultaneously by processing feature layers P2-P5 with different spatial resolutions. This multi-scale approach allows the model to detect objects of various sizes accurately while maintaining fast processing speeds through parallel computation across different dimensions.
3Measurement precision
If multi-scale feature fusion is implemented, then detection accuracy improves, but computational cost and hardware requirements increase
Solution Approach 1:
The system applies different processing qualities to different feature layers based on their local characteristics. Shallow features (P2-P3) use lighter processing for fast detection, while deep features (P4-P5) use heavier processing for precise detection. This local quality differentiation optimizes computational cost while maintaining high detection accuracy.
Solution Approach 2:
The system dynamically adjusts processing parameters for different feature layers through interpolation operations and addition combinations. By changing the processing intensity and computational parameters based on feature layer depth, the system achieves high accuracy without proportionally increasing computational cost.
4Measurement precision
If advanced detection algorithms are used to improve precision, then detection accuracy increases, but processing time and real-time capability deteriorate
Solution Approach 1:
The backbone network performs preliminary feature extraction and the neck module performs preliminary feature fusion before the detection heads process the features for final object detection. This preliminary action divides the computation into stages, allowing fast processing while maintaining precision through optimized feature representation.
Solution Approach 2:
The system uses detection heads that replicate the same architectural pattern across multiple scales (P2-P5), where each head processes its corresponding feature layer independently. This copying approach allows parallel processing and maintains consistent precision across different object sizes while improving overall processing speed.
Data Source
AI summary
An object detection method and an electronic apparatus are provided. A processor is configured to perform the following. An original image is received. A plurality of initial feature layers of different scales is extracted from the original image. A plurality of fused feature layers of different scales is obtained through an interpolation and an addition operation based on the initial feature layers. The fused feature layers are respectively input into corresponding detection heads to obtain a bounding box location probability distribution through a bounding box regression branch of the detection head and obtain a classification probability distribution through a classification branch of the detection head.