# Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems

arXiv: https://arxiv.org/abs/2201.01661 · HTML: https://mlsanity.com/paper/2201.01661/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](https://mlsanity.com/paper/1801.05944/) (2018)
- [Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)](https://mlsanity.com/paper/2109.09854/) (2021)

## Built on by (most cited first, 10 of 10)

- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
- [Rethinking Early-Fusion Strategies for Improved Multispectral Object Detection](https://mlsanity.com/paper/2405.16038/) (2024)
- [Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey](https://mlsanity.com/paper/2401.07801/) (2024)
- [ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection](https://mlsanity.com/paper/2209.09178/) (2022)
- [Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices](https://mlsanity.com/paper/2301.07613/) (2023)
- [Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems](https://mlsanity.com/paper/2307.13600/) (2023)
- [Data Center Audio/Video Intelligence on Device (DAVID) -- An Edge-AI Platform for Smart-Toys](https://mlsanity.com/paper/2311.11030/) (2023)
- [Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges](https://mlsanity.com/paper/2409.12977/) (2024)
- [Towards Privacy-Preserving Thermal Human Perception from Dataset to Deployment](https://mlsanity.com/paper/2610.14846/) (2026)

## Abstract (arXiv)

This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is a recorded from lost-cost yet effective uncooled LWIR thermal camera, mounted stand-alone and on an electric vehicle to minimize mechanical vibrations. State-of-the-art YOLO-V5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.