Vehicle Object Detection Using Color-Infrared Feature Pyramids
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Solution Overview
Problem
Current pedestrian detection systems in autonomous vehicles face challenges in environments with weak illumination, far distances, and occlusion, where existing technologies struggle to achieve accurate and real-time detection.
Innovation Solution
The system employs a combination of color and infrared cameras with a controller-circuit and processor that transforms image signals into classification and location data using a gated fusion unit, forming feature pyramids to enhance detection accuracy and location precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional two-stage detectors are used for pedestrian detection, then detection accuracy can be improved, but real-time performance deteriorates
Solution Approach 1:
The system segments the detection task by processing color and infrared image streams separately through dedicated convolutional neural networks, then fuses features at multiple pyramid levels. This parallel segmentation enables efficient real-time processing while maintaining high accuracy through multi-scale feature fusion.
Solution Approach 2:
The system introduces a temporal dimension by implementing multi-scale feature pyramid fusion across different processing stages. By fusing features at multiple pyramid levels (P2, P3, P4, P5) from both color and infrared streams, the system achieves comprehensive multi-scale coverage that improves accuracy without sacrificing real-time performance.
2Device complexity
If single sensor systems are used for object detection, then device complexity is reduced, but detection reliability in challenging environments deteriorates
Solution Approach 1:
The system merges color and infrared sensing modalities into a unified detection framework. By combining the complementary information from both sensors—color for texture and structure, infrared for thermal signatures—the system achieves superior reliability in challenging conditions while maintaining manageable complexity through shared network architecture.
Solution Approach 2:
The system creates a composite detection approach by fusing features from heterogeneous sensor modalities. The multi-scale feature pyramid integrates color and infrared features at multiple levels, creating a robust composite representation that maintains high detection reliability across varying environmental conditions.
3Measurement precision
If multi-scale feature fusion is implemented, then detection accuracy in various distances is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary feature extraction and pyramid construction separately for color and infrared streams before fusion. By pre-processing each modality independently and organizing features into standardized pyramid structures, the system reduces the computational complexity of the fusion operation itself while maintaining comprehensive multi-scale coverage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves pedestrian detection accuracy and location precision in challenging environments, achieving comparable or superior results to conventional two-stage detectors while maintaining real-time performance.
Implementation Method 1
receiving from a thermal image sensor, a thermal image signal indicative of a thermal image of the area
Data Source
AI summary
An object detection system includes color and infrared cameras, a controller-circuit, and instructions. The color and infrared cameras are configured to output respective color image and infrared image signals. The controller-circuit is in communication with the cameras, and includes a processor and a storage medium. The processor is configured to receive and transform the color image and infrared image signals into classification and location data associated with a detected object. The instructions are stored in the at least one storage medium and executed by the at least one processor, and are configured to utilize the color image and infrared image signals to form respective first and second maps. The first map has a first plurality of layers, and the second map has a second plurality of layers. Selected layers from each are paired and fused to form a feature pyramid that facilitates formulation of the classification and location data.