Cross-Modal Pedestrian Detection Calibration
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Solution Overview
Problem
Current multi-spectral pedestrian detectors face challenges in accurate detection due to color-thermal image differences, leading to inconsistent feature fusion, unstable inferences, and label deviations, especially under conditions of mismatched modal sensors and external interference.
Innovation Solution
A method for automatic calibration in cross-modal target detection involves obtaining feature maps from color-thermal images, extracting shared candidate regions, aligning them using RoIAlign and Smooth L1 loss, and employing a two-layer fully connected classifier for deviation regression and non-maximum suppression to stabilize pedestrian detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If maximum geometrical alignment is used for color-thermal images, then the data set can be constructed with shared annotations, but spatial misalignment and non-rigid transformations still occur in actual scenes due to sensor differences and external interference
Solution Approach 1:
The system performs preliminary geometric alignment on color-thermal image pairs during data set construction, pre-correcting obvious spatial mismatches. This preliminary action enables shared annotation creation while the detection model later learns to handle residual misalignments, dividing the alignment task between preprocessing and model learning
Solution Approach 2:
The system transforms the alignment problem from a geometric parameter space to a feature space by using learned feature representations. Instead of relying solely on precise geometric alignment, the model learns modal-specific and shared features that are robust to spatial variations, changing the problem parameters from coordinates to feature embeddings
2Productivity
If modal shared labels are used to frame both color and thermal targets, then annotation efficiency is improved, but label deviation is introduced for each individual modal
Solution Approach 1:
The system segments the annotation process into two levels: shared annotations at the multi-modal level for efficiency, and modal-specific refinements at the individual level for precision. The detection model learns to adjust shared labels for each modal separately, dividing the annotation function between shared and specific components
Solution Approach 2:
The system introduces an intermediary alignment layer that transforms shared annotations into modal-specific annotations. This intermediary process uses learned transformation parameters to adjust the shared bounding boxes for each modal's specific characteristics, mediating between the efficiency of shared labels and the precision of individual labels
3Speed
If cross-modal feature fusion is performed without alignment calibration, then detection speed is maintained, but feature inconsistency leads to unstable classification and positioning
Solution Approach 1:
The system changes the calibration approach from geometric parameter adjustment to feature space transformation. Instead of aligning images geometrically before fusion, the model learns feature representations that are inherently aligned in the feature space, transforming the problem from spatial parameters to feature parameters
Solution Approach 2:
The system performs preliminary feature extraction and alignment calibration before the main detection and fusion processes. By pre-computing alignment parameters and extracting aligned features in advance, the system prepares consistent input for the fusion network, maintaining speed during actual detection while ensuring reliability through pre-calibration
4Reliability
If traditional calibration and alignment processes are used for color thermal imagers, then sensor synchronization can be achieved, but the process becomes very complicated and requires specific hardware and calibration boards
Solution Approach 1:
The system replaces mechanical calibration hardware (calibration boards, specific alignment equipment) with computational methods. Instead of using physical tools to establish geometric relationships, the system uses learned transformation parameters from image data itself, substituting mechanical calibration with algorithmic calibration
Solution Approach 2:
The system enables the color-thermal imager to perform self-calibration using its own captured images. The calibration process uses the image data from the sensors themselves to learn alignment parameters, eliminating the need for external calibration equipment and making the system self-sufficient
Data Source
AI summary
A method, a system and a device for difference automatic calibration in cross modal target detection are provided. The method includes: obtaining a plurality of modals of color-thermal images to be detected, and constructing an input image set; acquiring a feature map of each modal image in the input image set, and extracting a shared candidate region; aligning the shared candidate region, and obtaining a feature representation of the each shared candidate region through pooling; obtaining a category of the feature representation of the shared candidate region based on a preset classifier, and obtaining a position of the shared candidate region through deviation regression; adopting non-maximum value suppression to obtain a shared candidate region with the highest probability according to the category of the feature representation of the shared candidate region, and returning to the image to be detected for marking according to the position of the shared candidate region.