Thermal Image Analysis and Multispectral Vision

Module: Computer Vision | Difficulty: Premium

Thermal Image Formation

where is emissivity, is object temperature.

Multispectral Fusion

where is spatially adaptive.

Cross-Modal Consistency Loss

| Model | KAIST mAP | FLIR mAP | Task | Approach | |-------|-----------|----------|------|----------| | Modality-Specific | 25.7 | 72.3 | Detection | Separate | | Fusion-at-early | 28.4 | 76.1 | Detection | Early fusion | | Fusion-at-features | 32.1 | 79.8 | Detection | Feature fusion | | Cross-Modal ViT | 35.2 | 82.4 | Detection | Attention |

import torch
import torch.nn as nn

class ThermalRGBFusion(nn.Module):
    def __init__(self, channels=64):
        super().__init__()
        self.rgb_encoder = nn.Sequential(
            nn.Conv2d(3, channels, 3, padding=1), nn.ReLU(inplace=True),
            nn.Conv2d(channels, channels, 3, padding=1), nn.ReLU(inplace=True),
        )
        self.thermal_encoder = nn.Sequential(
            nn.Conv2d(1, channels, 3, padding=1), nn.ReLU(inplace=True),
            nn.Conv2d(channels, channels, 3, padding=1), nn.ReLU(inplace=True),
        )
        self.attention = nn.Sequential(
            nn.Conv2d(channels * 2, channels, 1),
            nn.Sigmoid(),
        )
        self.fusion = nn.Sequential(
            nn.Conv2d(channels * 2, channels, 3, padding=1),
            nn.ReLU(inplace=True),
        )

    def forward(self, rgb, thermal):
        f_rgb = self.rgb_encoder(rgb)
        f_therm = self.thermal_encoder(thermal)
        attn = self.attention(torch.cat([f_rgb, f_therm], dim=1))
        fused = torch.cat([f_rgb * attn, f_therm * (1 - attn)], dim=1)
        return self.fusion(fused)

class TemperatureRegressor(nn.Module):
    def __init__(self, backbone_channels=64):
        super().__init__()
        self.regressor = nn.Sequential(
            nn.Conv2d(backbone_channels, 32, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 1, 1),
            nn.ReLU(inplace=True),
        )

    def forward(self, features):
        temp_map = self.regressor(features)
        return temp_map * 100 + 273.15

Research Insight: Thermal imaging provides unique information invisible to RGB cameras: heat signatures enable detection in complete darkness, through smoke, and for identifying thermal anomalies. The key challenge in thermal image analysis is the lack of large-scale labeled datasets. Self-supervised pretraining on thermal imagery (analogous to DINO for RGB) has shown promise for learning transferable thermal features. Cross-modal learning between RGB and thermal modalities enables knowledge distillation: RGB-pretrained models can guide thermal models, improving performance on thermal-only deployment scenarios.