Varying Lighting Conditions Research Articles

A novel heart rate estimation framework with self-correcting face detection for neonatal intensive care unit

Remote photoplethysmography (rPPG) is a non-invasive method for monitoring heart rate (HR) and other vital signs by measuring subtle facial color changes caused by blood flow variations beneath the skin, typically captured through video-based imaging. Current rPPG technology, which is optimized for ideal conditions, faces significant challenges in real-world clinical settings such as Neonatal Intensive Care Units (NICUs). These challenges primarily arise from the limitations of automatic face detection algorithms embedded in HR estimation frameworks, which have difficulty accurately detecting the faces of newborns. Additionally, variations in lighting conditions can significantly affect the accuracy of HR estimation. The combination of these positional changes and fluctuations in lighting significantly impacts the accuracy of HR estimation. To address the challenges of inadequate face detection and HR estimation in newborns, we propose a novel HR estimation framework that incorporates a Self-Correcting face detection module. Our HR estimation framework introduces an innovative rPPG value reference module to mitigate the effects of lighting variations, significantly reducing HR estimation error. The Self-Correcting module improves face detection accuracy by enhancing robustness to occlusions and position changes while automating the process to minimize manual intervention. Our proposed framework demonstrates notable improvements in both face detection accuracy and HR estimation, outperforming existing methods for newborns in NICUs.

Attention enhanced machine instinctive vision with human-inspired saliency detection

Salient object detection (SOD) enables machines to recognize and accurately segment visually prominent regions in images. Despite recent advancements, existing approaches often lack progressive fusion of low and high-level features, effective multi-scale feature handling, and precise boundary detection. Moreover, the robustness of these models under varied lighting conditions remains a concern. To overcome these challenges, we present Attention Enhanced Machine Instinctive Vision framework for SOD. The proposed framework leverages the strategy of Multi-stage Feature Refinement with Optimal Attentions-Driven Framework (MFRNet). The multi-level features are extracted from six stages of the EfficientNet-B7 backbone. This provides effective feature fusions of low and high-level details across various scales at the later stage of the framework. We introduce the Spatial-optimized Feature Attention (SOFA) module, which refines spatial features from three initial-stage feature maps. The extracted multi-scale features from the backbone are passed from the convolution feature transformation and spatial attention mechanisms to refine the low-level information. The SOFA module concatenates and upsamples these refined features, producing a comprehensive spatial representation of various levels. Moreover, the proposed Context-Aware Channel Refinement (CACR) module integrates dilated convolutions with optimized dilation rates followed by channel attention to capture multi-scale contextual information from the mature three layers. Furthermore, our progressive feature fusion strategy combines high-level semantic information and low-level spatial details through multiple residual connections, ensuring robust feature representation and effective gradient backpropagation. To enhance robustness, we train our network with augmented data featuring low and high brightness adjustments, improving its ability to handle diverse lighting conditions. Extensive experiments on four benchmark datasets—ECSSD, HKU-IS, DUTS, and PASCAL-S- validate the proposed framework’s effectiveness, demonstrating superior performance compared to existing SOTA methods in the domain. Code, qualitative results, and trained weights will be available to the research community at the link: https://github.com/habib1402/MFRNet-SOD.

Driver inattention detection system using multi-task cascaded convolutional networks

Driver inattention has emerged as a critical concern impacting road safety, resulting in an alarming surge in accidents and fatalities. This research introduces a novel system for detecting inattention, structured across six levels: perception, facial feature extraction, tracking driver face, and driver secondary task using pre-trained deep learning models, inattention detection, risk estimation, and alert. The system is based on image processing captured from two strategically positioned cameras that simultaneously capture the driver’s activities while driving and their facial expressions. The second contribution concerns the driver facial features extraction using multi-task cascaded convolutional networks (MTCNN), and it is comparison with the histogram of gradient (HOG)-based frontal face detector, and haar feature based cascade classifier. The algorithms were compared based on their runtime efficiency, robustness in handling varying lighting conditions, and various head movements. The MTCNN achieves high performance, reaching accuracy levels ranging from 96.4% to 99.5% on two datasets including realistic driving scenarios: the DrivFace dataset and, the driver drowsiness dataset. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, providing valuable insights for selecting the most suitable face detection algorithm to use in our system.

Developing WasteSAM: A novel approach for accurate construction waste image segmentation to facilitate efficient recycling

The escalating volume of construction activities and resultant waste generation underscores the imperative for developing sophisticated segmentation models to facilitate efficient sorting and recycling processes. This study introduces WasteSAM, an enhanced iteration of the segment anything model (SAM), specifically tailored to address the intricate complexities inherent in construction waste imagery. Drawing upon a comprehensive dataset comprising over 15,000 masks representing five distinct categories of construction materials, WasteSAM exhibits notably superior segmentation capabilities. Quantitative analysis demonstrates significant performance improvements, with WasteSAM outperforming the original SAM model by an average of 23.9% in dice similarity coefficient and 30.0% in normalized surface distance metrics. The integration of stereo-image techniques in refining the training dataset has facilitated WasteSAM in more accurately discerning the three-dimensional structure of waste materials, thereby augmenting the precision of waste classification. Noteworthy is the model’s adeptness in handling intricate textures and patterns across diverse imaging modalities, including varying lighting conditions and complex object interactions. While showing promising results, this study also highlights the need for high-quality, diverse datasets that reflect real-world construction site complexities, rather than merely larger datasets.

Design and Implementation of an AI-Based Robotic Arm for Strawberry Harvesting

This study presents the design and implementation of a wire-driven, multi-joint robotic arm equipped with a cutting and gripping mechanism for harvesting delicate strawberries, with the goal of reducing labor and costs. The arm is mounted on a lifting mechanism and linked to a laterally movable module, which is affixed to the tube cultivation shelf. The trained deep learning model can instantly detect strawberries, identify optimal picking points, and estimate the contour area of fruit while the mobile platform is in motion. A two-stage fuzzy logic control (2s-FLC) method is employed to adjust the length of the arm and bending angle, enabling the end of the arm to approach the fruit picking position. The experimental results indicate a 90% accuracy in fruit detection, an 82% success rate in harvesting, and an average picking time of 6.5 s per strawberry, reduced to 5 s without arm recovery time. The performance of the proposed system in harvesting strawberries of different sizes under varying lighting conditions is also statistically analyzed and evaluated in this paper.

Nitrogen at the crossroads of light: Integration of light signalling and plant nitrogen metabolism.

Plants have developed complex mechanisms to perceive, transduce, and respond to environmental signals, such as light, which are essential for acquiring and allocating resources, including nitrogen (N). This review delves into the complex interaction between light signals and N metabolism, emphasising light-mediated regulation of N uptake and assimilation. Firstly, we discuss the details of light-mediated regulation of N uptake and assimilation, focusing on the light-responsive activity of nitrate reductase (NR) and nitrate transporters. Secondly, we discuss the influence of light on N-dependent developmental plasticity, elucidating how N availability regulates crucial developmental transitions such as flowering time, shoot branching, and root growth, as well as how light modulates these processes. Additionally, we discuss the molecular interaction between light and N signalling, focusing on photoreceptors and transcription factors such as HY5, which are necessary for N uptake and assimilation under varying light conditions. A recent understanding of the nitrate signalling and perception of low N is also highlighted. The insilico transcriptome analysis suggests a reprogramming of N signalling genes by shade and identifies NLP7, bZIP1, CPK30, CBL1, LBD37, LBD38 and HRS1 as crucial molecular regulators integrating light-regulated N metabolism.

Identification and Expression Analysis of TCP Transcription Factors Under Abiotic Stress in Phoebe bournei.

The TCP gene family encodes plant transcription factors crucial for regulating growth and development. While TCP genes have been identified in various species, they have not been studied in Phoebe bournei (Hemsl.). This study identified 29 TCP genes in the P. bournei genome, categorizing them into Class I (PCF) and Class II (CYC/TB1 and CIN). We conducted analyses on the PbTCP gene at both the protein level (physicochemical properties) and the gene sequence level (subcellular localization, chromosomal distribution, phylogenetic relationships, conserved motifs, and gene structure). Most P. bournei TCP genes are localized in the nucleus, except PbTCP9 in the mitochondria and PbTCP8 in both the chloroplast and nucleus. Chromosomal mapping showed 29 TCP genes unevenly distributed across 10 chromosomes, except chromosome 8 and 9. We also analyzed the promoter cis-regulatory elements, which are mainly involved in plant growth and development and hormone responses. Notably, most PbTCP transcription factors respond highly to light. Further analysis revealed three subfamily genes expressed in five P. bournei tissues: leaves, root bark, root xylem, stem xylem, and stem bark, with predominant PCF genes. Using qRT-PCR, we examined six representative genes-PbTCP16, PbTCP23, PbTCP7, PbTCP29, PbTCP14, and PbTCP15-under stress conditions such as high temperature, drought, light exposure, and dark. PbTCP14 and PbTCP15 showed significantly higher expression under heat, drought, light and dark stress. We hypothesize that TCP transcription factors play a key role in growth under varying light conditions, possibly mediated by auxin hormones. This work provides insights into the TCP gene family's functional characteristics and stress resistance regulation in P. bournei.

Unique structural attributes of the PSI-NDH supercomplex in Physcomitrium patens.

Cyclic electron transport around photosystem I (PSI) is essential for the protection of the photosynthetic apparatus in plants under diverse light conditions. This process is primarily mediated by Proton Gradient Regulation 5 protein/Proton Gradient Regulation 5-like photosynthetic phenotype 1 protein (PGR5/PGRL1) and NADH dehydrogenase-like complex (NDH). In angiosperms, NDH interacts with two PSI complexes through distinct monomeric antennae, LHCA5 and LHCA6, which is crucial for its higher stability under variable light conditions. This interaction represents an advanced evolutionary stage and offers limited insight into the origin of the PSI-NDH supercomplex in evolutionarily older organisms. In contrast, the moss Physcomitrium patens (Pp), which retains the lhca5 gene but lacks the lhca6, offers a glimpse into an earlier evolutionary stage of the PSI-NDH supercomplex. Here we present structural evidence of the Pp PSI-NDH supercomplex formation by single particle electron microscopy, demonstrating the unique ability of Pp to bind a single PSI in two different configurations. One configuration closely resembles the angiosperm model, whereas the other exhibits a novel PSI orientation, rotated clockwise. This structural flexibility in Pp is presumably enabled by the variable incorporation of LHCA5 within PSI and is indicative of an early evolutionary adaptation that allowed for greater diversity at the PSI-NDH interface. Our findings suggest that this variability was reduced as the structural complexity of the NDH complex increased in vascular plants, primarily angiosperms. This study not only clarifies the evolutionary development of PSI-NDH supercomplexes but also highlights the dynamic nature of the adaptive mechanisms of plant photosynthesis.

Spiking Neural Networks for Real-Time Pedestrian Street-Crossing Detection Using Dynamic Vision Sensors in Simulated Adverse Weather Conditions

This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in dynamic, low-light, and high-contrast environments, this research evaluates the effectiveness of SNNs compared to traditional Convolutional Neural Networks (CNNs). The experimental setup involved a custom dataset from the CARLA simulator, designed to mimic real-world variability, including rain, fog, and varying lighting conditions. Additionally, the JAAD dataset was adopted to allow for evaluations using real-world data. The SNN models were optimized using Temporally Effective Batch Normalization (TEBN) and benchmarked against well-established deep learning models, concerning their accuracy, computational efficiency, and energy efficiency in complex weather conditions. This study also conducted a comprehensive analysis of energy consumption, highlighting the significant reduction in energy usage achieved by SNNs when processing DVS data. The results indicate that SNNs, when integrated with DVSs, not only reduce computational overhead but also dramatically lower energy consumption, making them a highly efficient choice for real-time applications in autonomous vehicles (AVs).

MoiréTag: A Low-Cost Tag for High-Precision Tangible Interactions without Active Components

In this paper, we present MoiréTag—a novel tag-like device that magnifies displacement without active components for indirect sensing of subtle tangible interactions. The device consists of two overlapping layers of stripe patterns with distinct pattern frequencies. These layers create Moiré fringes that can move faster than the actual movement of a layer. Using a customized image processing pipeline, we show that MoiréTag can reliably detect sub-mm movement in real-time (mean error = 0.043 mm) under varying lighting conditions, camera angles, and camera distances. We also demonstrate five applications of MoiréTag to showcase its potential as a low-cost solution to capture and monitor small changes in movement and other physical properties, such as force and volume, by converting them into displacement.

PcMNet: An efficient lightweight apple detection algorithm in natural orchards

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

Na+-driven pH regulation by Na+/H+ antiporters promotes photosynthetic efficiency in cyanobacteria.

Photosynthetic organisms have developed mechanisms to regulate light reactions in response to varying light conditions. Photosynthetic electron transport leads to the formation of a ΔpH across the thylakoid membrane, which is crucial for regulating electron transport. However, other pH modulators remain to be identified, particularly in cyanobacteria. In this study, we evaluated the potential involvement of six Na+/H+ antiporters (NhaS1-NhaS6) in control of pH in the cyanobacterium Synechocystis sp. PCC 6803. Synechocystis showed a strong requirement for Na+ at high light intensities, with ΔnhaS1 and ΔnhaS2 strains unable to grow under high light conditions. We analyzed Na+ efflux-driven H+-uptake activities of NhaS1-NhaS6 in inverted membranes of Escherichia coli. Biological fractionation and immunoelectron microscopy revealed that NhaS1 localizes to both the plasma and thylakoid membranes while NhaS2 localizes to the plasma membrane. Measurement of photosynthesis activity indicated that NhaS2 promotes ATP production and electron transport from PQ to P700. Measurements of pH outside of the cells and in the cytoplasm suggested that both NhaS1 and NhaS2 are involved in plasma membrane-mediated light-dependent H+ uptake and cytoplasmic acidification. NhaS1 and NhaS2 were also found to prevent photoinhibition under high light treatment. These results indicate that H+ transport mediated by NhaS1 and NhaS2 plays a role in regulating intracellular pH and maintaining photosynthetic electron transport.

Exploring patterns in older pedestrian involved crashes during nighttime

Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017–2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65–74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

The HAT1 transcription factor regulates photomorphogenesis and skotomorphogenesis via phytohormone levels.

Plants dynamically modulate their growth and development to acclimate to the fluctuating light environment via a complex phytohormone network. However, the dynamic molecular regulatory mechanisms underlying how plants regulate phytohormones during skotomorphogenesis and photomorphogenesis are largely unknown. Here, we identified a HD-ZIP II transcription factor, HOMEODOMAIN ARABIDOPSIS THALIANA1 (HAT1), as a key node that modulates the dose effects of brassinosteroids (BR) and auxin on hypocotyl growth during skotomorphogenesis and photomorphogenesis. Compared with the wild-type (Col-0), both HAT1 loss of function and its overexpression led to disrupted photomorphogenic and skotomorphogenic hypocotyl growth. HAT1 overexpression (HAT1OX) plants displayed longer hypocotyls in the light but shorter hypocotyls in darkness, whereas the triple mutant hat1hat2hat3 showed the opposite phenotype. Furthermore, we found that CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1) interacted with dephosphorylated HAT1 and facilitated the degradation of HAT1 by ubiquitination in darkness, while HAT1 was phosphorylated and stabilized by BRASSINOSTEROID INSENSITIVE2 (BIN2) in the light. Interestingly, we observed distinct dose-dependent effects of BR and auxin on hypocotyl elongation under varying light conditions and that HAT1 functioned as a key node in this process. The shorter hypocotyl of HAT1OX in darkness was due to the inhibition of BR biosynthetic gene BRASSINOSTEROID-6-OXIDASE2 (BR6OX2) expression to reduce BRs content, while brassinolide (BL) treatment alleviated this growth repression. In the light, HAT1 inhibited BR biosynthesis but enhanced auxin signaling by directly repressing IAA3/SHORT HYPOCOTYL 2 (SHY2) expression. Our findings uncover a dual function of HAT1 in regulating BR biosynthesis and auxin signaling that is crucial for ensuring proper skotomorphogenic and photomorphogenic growth.

Performance of spherical and flat solar cells under different environmental conditions

This study investigates the performance degradation of spherical and flat solar modules due to dust accumulation. To isolate the effect of dust, both modules were subjected to identical environmental conditions, including irradiance, temperature, and humidity. The results reveal a significant reduction in power efficiency as dust density increases, with a nearly linear relationship observed. Spherical solar modules exhibited a minor decrease in efficiency (±3.64%) compared to flat solar modules (±5.46%) under increased dust density, consistent with prior research. Dust accumulation affects solar cells by causing dust deposition, light scattering, and temperature rise. Flat solar panels are more susceptible to dust accumulation due to their larger surface area, which attracts more dust particles. In contrast, spherical cells’ smaller surface area minimizes dust build-up, making them less prone to efficiency loss. The study also examined the impact of solar intensity and tilt angle on efficiency. Spherical cells demonstrated greater resilience in varying light conditions and tilt angles, maintaining efficiency across a wider range of scenarios than flat panels. These findings emphasize the importance of considering dust accumulation when designing and maintaining solar panels, with spherical modules offering advantages in dusty environments and under changing sunlight conditions.

Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions

Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.

PDC-YOLO: A Network for Pig Detection under Complex Conditions for Counting Purposes

Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed.

Rapid monitoring of structural deformation based on unsupervised segmentation model

This paper presents a novel approach for rapid structural deformation monitoring using an enhanced segmentation model, SAM-DM (Segment Anything Model-Deformation Monitoring). The method enhances the SAM model by integrating an innovative point prompt generation technique, image fusion, and connected domain analysis, enabling structural deformation monitoring with just a single manual click on the target area in the initial video frame. Specifically, this manual click generates coordinate information for the target area, serving as a point prompt for the SAM-DM model. This process results in the creation of a high-quality binary mask, which is then fused with the original image to isolate only the target area. Subsequently, connected domain analysis is employed to automatically process the mask, extracting the pixel centroid coordinates of the target area, which are then used as prompts for segmenting subsequent frames. This approach allows continuous tracking of changes in the target area's coordinates, thereby facilitating rapid structural deformation monitoring. Experimental results demonstrate that this method can accurately monitor deformation across all target points and adapt to varying lighting conditions and noise levels. Applied to monitor the bending deformation of reinforced concrete beams and the in-plane deformation of concrete slabs, the method achieves results closely aligned with actual measurements, yielding relative errors of 0.95 % and 0.48 %, respectively. Additionally, in monitoring the vibrational deformation of steel frames, the primary frequency of the curve precisely matches measured values, confirming that the SAM-DM-based method delivers both high accuracy and strong robustness.

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.