Real-Time Object Detection Model Using Segmented Networks
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Solution Overview
Problem
Current real-time object detection technologies face challenges in efficiently processing images with moving pedestrians in dense environments, leading to high requirements for real-timeness and accuracy in scenarios like pedestrian traffic statistics.
Innovation Solution
A method utilizing an object real-time detection model comprising a feature extraction network, feature fusion network, and data obtaining network, with 5×5 depthwise separable convolution kernels, to preprocess images and determine object central points and sizes, combined with a performance optimization method using simulated annealing and gradient boosting decision trees to adjust processing resources for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection models are used to detect moving pedestrians in dense environments, then detection accuracy can be achieved, but processing time increases and real-time performance deteriorates
Solution Approach 1:
The detection model is segmented into three independent sub-networks: feature extraction network, feature fusion network, and data obtaining network. Each sub-network processes specific tasks independently, allowing parallel computation and reducing overall processing time while maintaining detection accuracy through coordinated information flow between segments.
Solution Approach 2:
The feature extraction network performs preliminary feature extraction on input images before the feature fusion network processes the extracted features. This preliminary action prepares and pre-processes the data in advance, reducing the computational burden on subsequent networks and enabling faster overall processing while preserving detection precision.
2Measurement precision
If complex feature fusion is applied to handle dense environments, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The feature fusion network applies different fusion strategies to different spatial regions and feature layers. Local quality is enhanced by selectively fusing features based on their spatial location and semantic importance, rather than uniformly processing all features. This reduces computational complexity by focusing computation on critical regions while maintaining overall detection accuracy.
Solution Approach 2:
The model transitions from 2D image processing to multi-scale feature space processing by incorporating spatial transformer modules and hierarchical feature fusion. This dimensional transformation allows the system to handle dense environments more effectively by operating in multiple feature dimensions simultaneously, improving accuracy without proportionally increasing computational complexity.
3Productivity
If real-time detection is implemented for pedestrian traffic statistics, then processing speed increases, but detection reliability decreases in dense environments
Solution Approach 1:
The data obtaining network incorporates feedback mechanisms that continuously monitor detection results and adjust feature extraction and fusion operations accordingly. This feedback loop enables the system to maintain high processing speed while improving reliability by adapting to challenging dense environment conditions in real-time, correcting detection errors dynamically.
Solution Approach 2:
The model employs dynamic adaptive normalization and spatial transformer modules that adjust their operation based on the complexity of the input image and detected object characteristics. This dynamic adaptation allows the system to optimize processing speed for simple scenes while automatically increasing reliability for complex dense environments, achieving both goals simultaneously.
Data Source
AI summary
The present disclosure relates to a method of performing real-time object detection using an object real-time detection model and a performance optimization method of object real-time detection model. According to an embodiment, the method of performing real-time object detection using an object real-time detection model includes: obtaining an identification image of a preset size by pre-processing an input image; obtaining object central point data and object size data by processing the identification image using the object real-time detection model; and obtaining an object detection result by determining an object region in the input image according to the object central point data and the object size data.