A Nighttime Driving Detection Model with Adaptive Growth Characteristics
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This study developed an adaptive nighttime driving detection model using CI-GAN with adaptive fine-tuning, which iteratively refines the generative network and discriminator to improve nighttime image generation and detection precision without compromising inference speed or model size.
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This preprint studies nighttime driving scene detection and proposes an adaptive computer-vision model to improve accuracy under low illumination, glare, and ambiguous datasets, without relying on nighttime-specific preprocessing. The method combines a Confidence Iterative Generative Adversarial Network (CI-GAN) that uses confidence iterative learning in daylight to steer generative behavior and continuous discriminator refinement at night to make generated nighttime images more realistic, along with Adaptive Fine-Tuning using differential optimization with transfer learning to automate fine-tuning layer selection. The authors report empirical analyses that the refined model improves nighttime detection while maintaining the original inference velocity and model dimensions. A major caveat is that the work is presented as a Research Square preprint and is not peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract Nighttime driving scene detection currently trails its daytime counterpart, largely due to challenges such as reduced illumination, glare, and ambiguous datasets. This research introduces an innovative adaptive model crafted to amplify nighttime driving detection precision. The model integrates a specially conceived Confidence Iterative Generative Adversarial Network (CI-GAN) with Adaptive Fine-Tuning, ensuring persistent model enhancement. While safeguarding the foundational features of the initial model, our strategy optimally acclimatizes the model to multifarious nighttime nuances. In daylight conditions, CI-GAN deploys a confidence iterative learning tactic to aptly steer the generative network, subsequently boosting its generative velocity. In tandem, during nocturnal periods, it ceaselessly refines its discriminator, prompting CI-GAN to produce images that bear a closer resemblance to authentic nighttime vistas. Adaptive Fine-Tuning incorporates a differential optimization technique grounded in transfer learning. This obviates the traditionally tedious manual layer selection process, instead harnessing the algorithm's expansive search potential to pinpoint the most effective fine-tuning methodology. Empirical analyses affirm that our refined model progressively elevates nighttime detection capabilities, all while maintaining the original model's inference velocity and dimensions. This advancement diverges from conventional methods that preprocess nighttime imagery prior to detection. Instead, it equips the model to dynamically evolve within nighttime environments, offering fresh insights and techniques for enhancing nighttime driving detection.
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A Nighttime Driving Detection Model with Adaptive Growth Characteristics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Nighttime Driving Detection Model with Adaptive Growth Characteristics Hang Ma, Genjian Yang, Wenbai Chen, Junzhuo Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4423745/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Nighttime driving scene detection currently trails its daytime counterpart, largely due to challenges such as reduced illumination, glare, and ambiguous datasets. This research introduces an innovative adaptive model crafted to amplify nighttime driving detection precision. The model integrates a specially conceived Confidence Iterative Generative Adversarial Network (CI-GAN) with Adaptive Fine-Tuning, ensuring persistent model enhancement. While safeguarding the foundational features of the initial model, our strategy optimally acclimatizes the model to multifarious nighttime nuances. In daylight conditions, CI-GAN deploys a confidence iterative learning tactic to aptly steer the generative network, subsequently boosting its generative velocity. In tandem, during nocturnal periods, it ceaselessly refines its discriminator, prompting CI-GAN to produce images that bear a closer resemblance to authentic nighttime vistas. Adaptive Fine-Tuning incorporates a differential optimization technique grounded in transfer learning. This obviates the traditionally tedious manual layer selection process, instead harnessing the algorithm's expansive search potential to pinpoint the most effective fine-tuning methodology. Empirical analyses affirm that our refined model progressively elevates nighttime detection capabilities, all while maintaining the original model's inference velocity and dimensions. This advancement diverges from conventional methods that preprocess nighttime imagery prior to detection. Instead, it equips the model to dynamically evolve within nighttime environments, offering fresh insights and techniques for enhancing nighttime driving detection. Nighttime Detection GAN Transfer Learning Fine-Tuning Autonomous Driving Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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