Learning deep feature fusion for traffic light detection
Journal of Engineering Research, ISSN: 2307-1877, Vol: 12, Issue: 1, Page: 100-106
2024
- 2Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Traffic light detection in real-world conditions is challenging because of the positioning of lights, variety in shapes and scales, and similarity with other objects. The paper presents a deep learning-based traffic light detection system by learning the fusion of handcrafted features. The handcrafted features for object detection focus on specific attributes such as shape, color, or texture. The objective of this work is to incorporate handcrafted features into the network learning process such that the resulting detector parameters are robust to input variations, sensor noise, and atmospheric noise. The proposed detection framework is based on the latest You only look once (YOLO) architecture, trained with the fusion of different information channels in the Integral Channel Features (ICF). The approach demonstrates a qualitative approach for identifying the optimal layer for additional feature injection in the network, and the selection of ICF channels to be applied for fusion. The validation of the proposed detector on the Bosch small traffic light dataset achieved the best mAP score of 55.70 % on the testing set. Further, a qualitative comparison of the proposed detector’s performance with that of other recent methods is presented, along with an analysis using auxiliary experiments.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2307187723000676; http://dx.doi.org/10.1016/j.jer.2023.100066; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85168156444&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2307187723000676; https://dx.doi.org/10.1016/j.jer.2023.100066
Elsevier BV
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