ArticleSensors (Basel, Switzerland)2022
Adopting the YOLOv4 Architecture for Low-Latency Multispectral Pedestrian Detection in Autonomous Driving.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 75 citations in OpenAlex.
- Fusion of Visible and Infrared Aerial Images from Uncalibrated Sensors Using Wavelet Decomposition and Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions.Sensors (Basel, Switzerland) · 2024Review
- Sensor-Fused Nighttime System for Enhanced Pedestrian Detection in ADAS and Autonomous Vehicles.Sensors (Basel, Switzerland) · 2024Article
- Object Detection, Recognition, and Tracking Algorithms for ADASs-A Study on Recent Trends.Sensors (Basel, Switzerland) · 2023Review
- Multispectral Benchmark Dataset and Baseline for Forklift Collision Avoidance.Sensors (Basel, Switzerland) · 2022Article
- Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets.Sensors (Basel, Switzerland) · 2022Article
- A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices.Sensors (Basel, Switzerland) · 2022Article
- YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection.Sensors (Basel, Switzerland) · 2022Article
- An Unsupervised Transfer Learning Framework for Visible-Thermal Pedestrian Detection.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Detecting pedestrians in autonomous driving is a safety-critical task, and the decision to avoid a a person has to be made with minimal latency. Multispectral approaches that combine RGB and thermal images are researched extensively, as they make it possible to gain robustness under varying illumination and weather conditions. State-of-the-art solutions employing deep neural networks offer high accuracy of pedestrian detection. However, the literature is short of works that evaluate multispectral pedestrian detection with respect to its feasibility in obstacle avoidance scenarios, taking into account the motion of the vehicle. Therefore, we investigated the real-time neural network detector architecture You Only Look Once, the latest version (YOLOv4), and demonstrate that this detector can be adapted to multispectral pedestrian detection. It can achieve accuracy on par with the state-of-the-art while being highly computationally efficient, thereby supporting low-latency decision making. The results achieved on the KAIST dataset were evaluated from the perspective of automotive applications, where low latency and a low number of false negatives are critical parameters. The middle fusion approach to YOLOv4 in its Tiny variant achieved the best accuracy to computational efficiency trade-off among the evaluated architectures.
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