@article{bouafia_ses-renet_2025,

title = {SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions},

author = {Y. Bouafia and M. S. Allili and L. Hebbache and L. Guezouli},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208562795&doi=10.1016%2fj.image.2024.117223&partnerID=40&md5=322b79f8d78045395efcabcbf86c6e1c},

doi = {10.1016/j.image.2024.117223},

issn = {09235965 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Signal Processing: Image Communication},

volume = {130},

abstract = {Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024},

note = {Publisher: Elsevier B.V.},

keywords = {Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities},

pubstate = {published},

tppubtype = {article}

}