Cascade Object Detection Network Parameter Reduction
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Solution Overview
Problem
Existing object detection networks require processing a large number of parameters, leading to low object detection efficiency.
Innovation Solution
The proposed object detection method employs a multi-layer cascade network architecture, where feature maps are extracted using a combination of convolution and linear transformation, reducing the number of parameters processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional object detection networks are used, then object detection accuracy can be maintained, but the number of parameters processed is large leading to low detection efficiency
Solution Approach 1:
The network is divided into multiple cascade stages, where each stage processes features at different levels of abstraction. Early stages handle low-level features with fewer parameters, while later stages refine detections with progressively fewer parameters as false positives are eliminated, thereby reducing the total parameter processing burden while maintaining detection accuracy
Solution Approach 2:
The cascade structure applies partial detection actions at each stage - early stages perform broad detection with simpler parameters, and subsequent stages apply more refined detection only to regions of interest identified in previous stages. This partial action approach reduces the overall number of parameters processed compared to applying full detection uniformly across the entire image
2Measurement precision
If more parameters are processed in the detection network, then detection accuracy may improve, but the processing time increases reducing efficiency
Solution Approach 1:
The cascade network performs preliminary detection actions in early stages using fewer parameters to identify potential object regions. These preliminary results guide subsequent stages to focus computational resources only on promising regions, avoiding the need to process all parameters across the entire image and thereby reducing total processing time while maintaining detection accuracy
Solution Approach 2:
The network dynamically adjusts the amount of parameter processing at each cascade stage based on detection confidence from previous stages. High-confidence detections from early stages require minimal further processing, while low-confidence regions receive more intensive parameter analysis in later stages, optimizing the balance between accuracy and processing time
Data Source
AI summary
An object detection method includes: obtaining a video to be detected; preprocessing the video to be detected to obtain an image to be detected; inputting the image to be detected into an object detection network; extracting, by the object detection network, a feature map of the image to be detected; performing, by the object detection network, an object prediction on the extracted feature map to obtain a position of an object in the image to be detected and a confidence degree corresponding to the position; and generating a marked object video according to the position of the object in the image to be detected, the confidence degree corresponding to the position, and the video to be detected.