Low-light Environments Research Articles

Lightweight enhanced YOLOv8n underwater object detection network for low light environments.

In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model's perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection.

Hierarchical Image Quality Improvement Based on Illumination, Resolution, and Noise Factors for Improving Object Detection

Object detection performance is significantly impacted by image quality factors such as illumination, resolution, and noise. This paper proposes a hierarchical image quality improvement process that dynamically prioritizes these factors based on severity, enhancing detection accuracy in diverse conditions. The process evaluates each factor—illumination, resolution, and noise—using discriminators that analyze brightness, edge strength, and noise levels. Improvements are applied iteratively with an adaptive weight update mechanism that adjusts factor importance based on improvement effectiveness. Following each improvement, a quality assessment is conducted, updating weights to fine-tune subsequent adjustments. This allows the process to learn optimal parameters for varying conditions, enhancing adaptability. The image improved through the proposed process shows improved quality through quality index (PSNR, SSIM) evaluation, and the object detection accuracy is significantly improved when the performance is measured using deep learning models called YOLOv8 and RT-DETR. The detection rate is improved by 7% for the ‘Bottle’ object in a high-light environment, and by 4% and 2.5% for the ‘Bicycle’ and ‘Car’ objects in a low-light environment, respectively. Additionally, segmentation accuracy saw a 9.45% gain, supporting the effectiveness of this method in real-world applications.

Advanced photodetector for hybrid PET-MRI systems

Background Currently, a wide variety of silicon photomultipliers (SiPMs) are available, each designed for specific applications in fields such as science, medicine, and industry. Advances in production technology have led to the development of more sensitive and efficient photodiodes, which are critical for applications requiring precision, such as medical imaging. Methods A research group has been working on designing a highly sensitive photodiode to enhance the capabilities of next-generation PET-MRI scanners. This involves integrating micropixel avalanche photodiodes (MAPDs) to improve image resolution. The chosen design features deep-immersion MAPDs with a pixel size of 12 microns and a density of 1000 pixels per square millimeter, allowing for high-detail photon detection. The 4x4 mm2 active area is optimized to balance sensitivity and size for high-resolution medical imaging. To produce these photodiodes, the group has outlined a production plan involving 20-inch silicon wafers grown using multiple techniques to enhance material properties. The Malaysian Institute of Microelectronic Systems (MIMOS), renowned for its expertise in optical microelectronics, was selected as the production center. With MIMOS' state-of-the-art facilities, the project aims to meet stringent medical diagnostics standards. Results The experimental results demonstrated that the MAPD-3NM photodiode achieved an amplification factor 1.8 times greater than the MAPD-3NK under optimal conditions. Its overvoltage range increased by 100%, reaching 4 V, enhancing photon detection and amplification. The MAPD-3NM also showed a significant reduction in dark current, about 3.5 times lower than the MAPD-3NK, improving performance in low-light environments. Additionally, the MAPD-3NM had a capacitance of 200 pF compared to 176 pF for the MAPD-3NK, contributing to its superior performance. These improvements make the MAPD-3NM more efficient and sensitive for scientific and medical applications. Conclusions This project represents a major advancement in photodetector technology for medical diagnostics, aiming to develop more accurate and efficient PET-MRI scanners that enhance patient outcomes with improved imaging capabilities.

An Improved UNet-Based Path Recognition Method in Low-Light Environments

The fruit industry is a significant economic sector in China, with modern orchards gradually transitioning to trellis orchards. For mechanized orchard operations, automatic vehicle navigation is essential. However, in trellis orchards, the shading from trees results in low average light intensity and large variations in lighting, posing challenges for path navigation. To address this, a path navigation algorithm for trellis orchards is proposed based on the UNet-CBAM model. The network structures of UNet, FCN, and SegNet are compared to identify and select the optimal structure for further improvement. Among the three attention mechanisms of channel attention, spatial attention, and combined attention, the most effective mechanism is identified. The optimal attention mechanism is incorporated into the optimized network to enhance the model’s ability to detect path edges and improve detection performance. To validate the effectiveness and generalizability of the model, a total of 400 images were collected under varying lighting intensities. The experimental results show that this method achieves an accuracy of 97.63%, a recall of 93.94%, and an Intersection over Union (IoU) of 92.19%. These results significantly enhance path recognition accuracy in trellis orchards, particularly under low light under conditions. Compared to the FCN and SegNet algorithms, this method provides higher detection accuracy and offers a new theoretical foundation and research approach for path recognition in low-light environments.

Robot Localization Method Based on Multi-Sensor Fusion in Low-Light Environment

When robots perform localization in indoor low-light environments, factors such as weak and uneven lighting can degrade image quality. This degradation results in a reduced number of feature extractions by the visual odometry front end and may even cause tracking loss, thereby impacting the algorithm’s positioning accuracy. To enhance the localization accuracy of mobile robots in indoor low-light environments, this paper proposes a visual inertial odometry method (L-MSCKF) based on the multi-state constraint Kalman filter. Addressing the challenges of low-light conditions, we integrated Inertial Measurement Unit (IMU) data with stereo vision odometry. The algorithm includes an image enhancement module and a gyroscope zero-bias correction mechanism to facilitate feature matching in stereo vision odometry. We conducted tests on the EuRoC dataset and compared our method with other similar algorithms, thereby validating the effectiveness and accuracy of L-MSCKF.

Oxidative stress levels and antioxidant defense mechanisms (Nrf2-Keap1 signaling pathway) in the Harderian glands of hibernating Daurian ground squirrels

Cyclic hibernation bouts in Daurian ground squirrels (Spermophilus dauricus) lead to repeated suppression and recovery of mitochondrial respiratory function across multiple organs, potentially impacting reactive oxygen species (ROS) dynamics. The Harderian gland (HG) plays an important role in endocrine regulation through porphyrin secretion. However, the influence of hibernation on oxidative pressure and associated antioxidant pathways in the HG remains inadequately understood. In the current study, we investigated the morphological changes, secretory activity, ROS levels, and underlying mechanisms in the HG of Daurian ground squirrels at distinct circannual stages of hibernation. Results indicated that: (1) Protoporphyrin levels in the HG increased during hibernation compared to the summer active (SA) phase, with a reduction in acinar lumen during torpor, potentially related to hibernation in a low-light environment. (2) Hydrogen peroxide (H2O2) and malondialdehyde (MDA) content during hibernation and post-hibernation (POST) did not exceed the levels observed in SA, indicating that the HG effectively mitigated oxidative pressure and lipid peroxidation during these periods. (3) Superoxide dismutase (SOD) activity increased while glutathione peroxidase (GPx) activity decreased during IBA compared to both SA and torpor, although total antioxidant capacity (T-AOC) remained stable across all stages. (4) Overall fluorescent intensity of nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) was significantly lower than in SA. These findings demonstrate that the HG in Daurian ground squirrels maintains a favorable oxidative status through the regulation of antioxidant enzyme activities during hibernation and even post-hibernation.

PII-GCNet: Lightweight Multi-Modal CNN Network for Efficient Crowd Counting and Localization in UAV RGB-T Images

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment.

Attendance Marking using Face Detection

The demand for automated attendance management systems has significantly increased in educational institutions and corporate environments where manual methods are prone to inefficiencies and errors. This paper presents a real-time, facial recognition-based attendance system developed using Flask, OpenCV, and machine learning techniques. The system employs OpenCV’s Haar Cascade classifier for face detection and the K-Nearest Neighbours (KNN) algorithm for face recognition. During user registration, the system captures 10 images per individual to ensure robust recognition across various facial expressions. The web interface, built using Flask, facilitates user interaction for managing attendance records stored in CSV format. The system achieves an accuracy of 92% under optimal lighting conditions, though performance decreases to 80% in low-light environments. Recognition is completed in approximately 2 seconds, making the system suitable for real-time applications in classrooms or offices. Despite handling real-time image streaming efficiently, challenges such as reduced accuracy when handling obstructions like masks or glasses remain. Future enhancements will explore the integration of deep learning models, such as Convolutional Neural Networks (CNNs), to improve robustness and scalability. This system demonstrates the potential of facial recognition technology for automating attendance tracking, with significant applications in both educational and corporate sectors

Carex parva and Carex scabrirostris adopt diverse response strategies to adapt to low-light conditions.

In recent years, the visible light intensity of lawns has significantly decreased due to obstructions caused by urban shading objects. Carex has a competitive advantage over other turfgrass in low-light conditions and extensive management. Therefore, exploring their survival strategy in low-light environments is of great significance. This study focuses on two species of Carex, Carex parva and Carex scabrirostris, and investigates their response to low-light conditions (150 μmol/m2/s) by simulating urban lawn conditions. Biomass allocation characteristics, leaf anatomical features, biochemical parameters, root morphology and photosynthetic parameters were measured. (a) Peroxidase activity, specific leaf area, and relative water content are key factors influencing the photosynthetic capacity of the two Carex species. (b) Under low-light conditions, photosynthetic parameters, leaf physiological indicators, and biomass allocation of the two Carex species were significantly affected (p<0.05). Both Carex species increased their investment in leaf biomass, maintained lateral root growth, and cleared reactive oxygen species to maintain their physiological balance. (c) In the simulated urban low-light environment, neither C. parva nor C. scabrirostris produced dauciform roots. In terms of response strategies, C. scabrirostris is a high-photosynthesis investing species with high productivity under low-light conditions, whereas C. parva exhibits minimal response, indicating a slow investment. C. scabrirostris has greater potential for application in low-light environments compared to C. parva. These results provide a theoretical basis for the cultivation and application of these two Carex species, as well as the expansion of turfgrass germplasm resources.

Low-light image enhancement guided by multi-domain features for detail and texture enhancement

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.

Low-Light Image Enhancement Network Using Informative Feature Stretch and Attention

Low-light images often exhibit reduced brightness, weak contrast, and color distortion. Consequently, enhancing low-light images is essential to make them suitable for computer vision tasks. Nevertheless, addressing this task is particularly challenging because of the inherent constraints posed by low-light environments. In this study, we propose a novel low-light image enhancement network using adaptive feature stretching and informative attention. The proposed network architecture mainly includes an adaptive feature stretch block designed to extend the narrow range of image features to a broader range. To achieve improved image restoration, an informative attention block is introduced to assign weight to the output features from the adaptive feature stretch block. We conduct comprehensive experiments on widely used benchmark datasets to assess the effectiveness of the proposed network. The experimental results show that the proposed low-light image enhancement network yields satisfactory results compared with existing state-of-the-art methods from both subjective and objective perspectives while maintaining acceptable network complexity.

Marking-Based Perpendicular Parking Slot Detection Algorithm Using LiDAR Sensors

The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions.

Research on Defect Detection Method of Fusion Reactor Vacuum Chamber Based on Photometric Stereo Vision.

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber.

Night time Surveillance in Communication Using YOLOv3

Nighttime surveillance presents significant challenges due to low visibility, varying lighting conditions, and interference from artificial light sources. Hence, this study proposed an effective object detection framework using the YOLOv3 model to enhance real-time monitoring in night surveillance applications, specifically within communication and security systems. YOLOv3's architecture, with its multi-scale detection and use of predefined anchor boxes, enables robust detection of objects under low-light environments and amidst light interference from vehicles and streetlights. The proposed system is tested on a night surveillance dataset, where it demonstrates high precision and speed in identifying objects, making it suitable for real-time applications. With Mean Average Precision (mAP) 87.9%, YOLOv3 effectively balances detection accuracy with inference time, ensuring minimal latency in live surveillance feeds. The results indicate that YOLOv3 outperforms traditional models such as Faster RCNN, particularly in detecting small objects under poor illumination. This approach offers a reliable solution for enhancing communication and security systems in nighttime surveillance scenarios.

Response of blueberry photosynthetic physiology to light intensity during different stages of fruit development.

To investigate the response of blueberry photosynthetic physiology to different light intensities during different stages of fruit development. In this study, four light intensity treatments (25%, 50%, 75% and 100% of full light) were set up to study the change rule of photosynthetic pigment content and photosynthetic characteristics of 'O'Neal' southern highbush blueberry leaves during the white fruiting stage (S1), purple fruiting stage (S2) and blue fruiting stage (S3) under different light intensity environments, and to explore the light demand and light adaptability of blueberry during different developmental stages of the fruit. The results showed that the chlorophyll and carotenoid contents of blueberry leaves showed an increasing trend with decreasing light intensity at all three stages of fruit development. The total chlorophyll content of blueberry leaves at 25% light intensity increased by 76.4% compared with CK during the blue fruiting stage; the maximum net photosynthetic rate (Pmax), light compensation point (LCP), light saturation point (LSP), rate of dark respirations (Rd), inter-cellular CO2 concentration (Ci), stomatal conductance (Gs), transpiration rate (Tr), net photosynthesis rate (Pn), and chlorophyll a/b showed a decreasing trend with decreasing light intensity. The Pn of blueberry leaves was highest under full light conditions at all three stages, and the Pn at 25% light intensity decreased by 68.5% compared with CK during the white fruiting stage Reflecting the fact that blueberries can adapt to low-light environments through increases in chlorophyll and carotenoids, but reduced light intensity significantly inhibited their photosynthesis. The photosynthetic physiology of blueberry showed a consistent pattern at all three stages, but there were some differences in the changes of photosynthetic parameters at different stages. The results of the study can provide theoretical references for the selection of sites and density regulation in blueberry production.

Enhancing Detection of Pedestrians in Low-Light Conditions by Accentuating Gaussian–Sobel Edge Features from Depth Maps

Owing to the low detection accuracy of camera-based object detection models, various fusion techniques with Light Detection and Ranging (LiDAR) have been attempted. This has resulted in improved detection of objects that are difficult to detect due to partial occlusion by obstacles or unclear silhouettes. However, the detection performance remains limited in low-light environments where small pedestrians are located far from the sensor or pedestrians have difficult-to-estimate shapes. This study proposes an object detection model that employs a Gaussian–Sobel filter. This filter combines Gaussian blurring, which suppresses the effects of noise, and a Sobel mask, which accentuates object features, to effectively utilize depth maps generated by LiDAR for object detection. The model performs independent pedestrian detection using the real-time object detection model You Only Look Once v4, based on RGB images obtained using a camera and depth maps preprocessed by the Gaussian–Sobel filter, and estimates the optimal pedestrian location using non-maximum suppression. This enables accurate pedestrian detection while maintaining a high detection accuracy even in low-light or external-noise environments, where object features and contours are not well defined. The test evaluation results demonstrated that the proposed method achieved at least 1–7% higher average precision than the state-of-the-art models under various environments.

Sequential Two-Mode Fusion Underwater Single-Photon Lidar Imaging Algorithm

Aiming at the demand for long-range and high-resolution imaging detection of small targets such as submerged submarine markers in shallow coastal waters, research on single-photon lidar imaging technology is carried out. This paper reports the sequential two-mode fusion imaging algorithm, which has a strong information extraction capability and can reconstruct scene target depth and reflection intensity images from complex signal photon counts. The algorithm consists of four steps: data preprocessing, extremely large group value estimation, noise sieving, and total variation smoothing constraints to image the target with high quality. Simulation and test results show that the imaging performance and imaging characteristics of the method are better than the current high-performance first-photon group imaging algorithm, indicating that the method has a great advantage in sparse photon counting imaging, and the method proposed in this paper constructs a clear depth and reflectance intensity image of the target scene, even in the 50,828 Lux ambient strong light and strong interference, the 0.1 Lux low-light environment, or the underwater high-attenuation environment.

RFSC-net: Re-parameterization forward semantic compensation network in low-light environments

Although detectors currently perform well in well-light conditions, their accuracy decreases due to insufficient object information. In addressing this issue, we propose the Re-parameterization Forward Semantic Compensation Network (RFSC-Net). We propose the Reparameterization Residual Efficient Layer Aggregation Networks (RSELAN) for feature extraction, which integrates the concepts of re-parameterization and the Efficient Layer Aggregation Networks (ELAN). While focusing on the fusion of feature maps of the same dimension, it also incorporates upward fusion of lower-level feature maps, enhancing the detailed texture information in higher-level features. Our proposed Forward Semantic Compensation Feature Fusion (FSCFF) network reduces interference from high-level to low-level semantic information, retaining finer details to improve detection accuracy in low-light conditions. Experiments on the low-light ExDark and DarkFace datasets show that RFSC-Net improves mAP by 2% on ExDark and 0.5% on DarkFace over the YOLOv8n baseline, without an increase in parameter counts. Additionally, AP50 is enhanced by 2.1% on ExDark and 1.1% on DarkFace, with a mere 3.7 ms detection latency on ExDark.

GOI-YOLOv8 Grouping Offset and Isolated GiraffeDet Low-Light Target Detection.

In the realm of computer vision, object detection holds significant importance and has demonstrated commendable performance across various scenarios. However, it typically requires favorable visibility conditions within the scene. Therefore, it is imperative to explore methodologies for conducting object detection under low-visibility circumstances. With its balanced combination of speed and accuracy, the state-of-the-art YOLOv8 framework has been recognized as one of the top algorithms for object detection, demonstrating outstanding performance results across a range of standard datasets. Nonetheless, current YOLO-series detection algorithms still face a significant challenge in detecting objects under low-light conditions. This is primarily due to the significant degradation in performance when detectors trained on illuminated data are applied to low-light datasets with limited visibility. To tackle this problem, we suggest a new model named Grouping Offset and Isolated GiraffeDet Target Detection-YOLO based on the YOLOv8 architecture. The proposed model demonstrates exceptional performance under low-light conditions. We employ the repGFPN feature pyramid network in the design of the feature fusion layer neck to enhance hierarchical fusion and deepen the integration of low-light information. Furthermore, we refine the repGFPN feature fusion layer by introducing a sampling map offset to address its limitations in terms of weight and efficiency, thereby better adapting it to real-time applications in low-light environments and emphasizing the potential features of such scenes. Additionally, we utilize group convolution to isolate interference information from detected object edges, resulting in improved detection performance and model efficiency. Experimental results demonstrate that our GOI-YOLO reduces the parameter count by 11% compared to YOLOv8 while decreasing computational requirements by 28%. This optimization significantly enhances real-time performance while achieving a competitive increase of 2.1% in Map50 and 0.6% in Map95 on the ExDark dataset.

IoTSLE: Securing IoT systems in low-light environments through finite automata, deep learning and DNA computing based image steganographic model

The Internet of Things (IoT) is a vast network of interconnected devices and systems, including wearables, smart home appliances, industrial machinery, and vehicles, equipped with sensors and connectivity. The data collected by the IoT devices are transmitted over a network, for processing and analyzing that data, so that appropriate actions can be initiated. Security of IoT systems is a major concern, as IoT devices collect and transmit crucial information. But images captured in low-light environments pose a challenge for IoT security by limiting the ability to accurately identify objects and people, increasing the risk of spoofing, and hindering forensic analysis. This paper unfolds a novel framework for IoT security using Steganography in Low-light Environment (IoTSLE) by image enhancement and data concealment. In proposed IoTSLE, initially, the low-light images, captured by the IoT devices in a low-light environment, are enhanced by band learning with recursion and band recomposition. After that, the secret information is concealed within the enhanced image. This concealment is supervised by using a specially designed finite automata for genome sequence encoding and 2-2-2 embedding. The proposed steganography technique is capable of hiding secret information within a 512 × 512 RGB image with the payload of 2097152 bits. The experiments like, PSNR, SSIM, Q-Index, BER, NCC, and NAE etc. are conducted to analyze the imperceptibility and security of IoTSLE. The proposed IoTSLE is useful for various IoT systems in different private and government fields like, defense agencies, digital forensics, agriculture, healthcare industry, cybersecurity firms, smart home, smart city etc.