Foggy Research Articles

A Multi-Tiered Collaborative Network for Optical Remote Sensing Fine-Grained Ship Detection in Foggy Conditions

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

DNE-YOLO: A method for apple fruit detection in Diverse Natural Environments

The apple industry, recognized as a pivotal sector in agriculture, increasingly emphasizes the mechanization and intelligent advancement of picking technology. This study innovatively applies a mist simulation algorithm to apple image generation, constructing a dataset of apple images under mixed sunny, cloudy, drizzling and foggy weather conditions called DNE-APPLE. It introduces a lightweight and efficient target detection network called DNE-YOLO. Building upon the YOLOv8 base model, DNE-YOLO incorporates the CBAM attention mechanism and CARAFE up-sampling operator to enhance the focus on apples. Additionally, it utilizes GSConv and the dynamic non-monotonic focusing mechanism loss function WIOU to reduce model parameters and decrease reliance on dataset quality. Extensive experimental results underscore the efficacy of the DNE-YOLO model, which achieves a detection accuracy (precision) of 90.7%, a recall of 88.9%, a mean accuracy (mAP50) of 94.3%, a computational complexity (GFLOPs) of 25.4G, and a parameter count of 10.46M across various environmentally diverse datasets. Compared to YOLOv8, it exhibits superior detection accuracy and robustness in sunny, drizzly, cloudy, and misty environments, making it especially suitable for practical applications such as apple picking for agricultural robots. The code for this model is open source at https://github.com/wuhaitao2178827/DNE-YOLO.

Low-light enhancement network for nighttime highway visibility estimation in foggy weather

Low-light enhancement network for nighttime highway visibility estimation in foggy weather

Object and Pedestrian Detection on Road in Foggy Weather Conditions by Hyperparameterized YOLOv8 Model

Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.

Offshore Ship Detection in Foggy Weather Based on Improved YOLOv8

Offshore Ship Detection in Foggy Weather Based on Improved YOLOv8

Style Optimization Networks for real-time semantic segmentation of rainy and foggy weather

Semantic segmentation is an essential task in the field of computer vision. Existing semantic segmentation models can achieve good results under good weather and lighting conditions. However, when the external environment changes, the effectiveness of these models are seriously affected. Therefore, we focus on the task of semantic segmentation in rainy and foggy weather. Fog is a common phenomenon in rainy weather conditions and has a negative impact on image visibility. Besides, to make the algorithm satisfy the application requirements of mobile devices, the computational cost and the real-time requirement of the model have become one of the major points of our research. In this paper, we propose a novel Style Optimization Network (SONet) architecture, containing a Style Optimization Module (SOM) that can dynamically learn style information, and a Key information Extraction Module (KEM) that extracts important spatial and contextual information. This can improve the learning ability and robustness of the model for rainy and foggy conditions. Meanwhile, we achieve real-time performance by using lightweight modules and a backbone network with low computational complexity. To validate the effectiveness of our SONet, we synthesized CityScapes dataset for rainy and foggy weather and evaluated the accuracy and complexity of our model. Our model achieves a segmentation accuracy of 75.29% MIoU and 83.62% MPA on a NVIDIA TITAN Xp GPU. Several comparative experiments have shown that our SONet can achieve good performance in semantic segmentation tasks under rainy and foggy weather, and due to the lightweight design of the model we have a good advantage in both accuracy and model complexity.

Performance analysis of hard-switching FSO/RF hybrid link system in foggy weather conditions

Performance analysis of hard-switching FSO/RF hybrid link system in foggy weather conditions

A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather

Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, contrast, tone, and sharpen, to remove fog and enhance image quality. However, determining the hyperparameters of these filters is challenging. We employ a multi-scale adaptive module to optimize filter hyperparameters, enhancing fog removal and image quality. Subsequently, YOLO is utilized to detect obstacles on rail transit tracks. The experimental results are encouraging, demonstrating the effectiveness of our proposed method in foggy scenarios.

Defog YOLO for road object detection in foggy weather

Abstract Object detection research predominantly focuses on clear weather conditions, often overlooking the challenges posed by foggy weather. Fog impairs the vision of onboard cameras, creating significant obstacles for autonomous vehicles. To tackle these issues, we present the Defog YOLO algorithm, specifically designed for road object detection in foggy conditions. Our approach integrates an enhanced U-Net framework for visual defogging, where the encoder leverages super-resolution back projection to combine multi-layer features. The decoder employs a back projection feedback mechanism to improve image restoration. Additionally, we augment the Feature Pyramid Network with a noise-aware attention mechanism, allowing the network to emphasize critical channel and spatial information while mitigating noise. Given the scarcity of labeled foggy images, we introduce a fog addition module to generate a more diverse training dataset. We validate our method using a synthesized FOG-TRAINVAL dataset, derived from the VOC dataset, demonstrating its robustness in foggy scenarios. Experimental results show that our proposed method achieves an mAP score of 60% on the Real-world Task-driven Testing Set foggy weather test set, with a precision of 86.7% and a recall of 54.2%. These findings underscore the effectiveness and improved generalizability of our approach for object detection in adverse weather conditions.

Nighttime agglomerate fog event detection considering car light glare based on video

Agglomerate fog event poses more serious threat than normal foggy weather to expressway traffic safety, due to its localized nature and suddenly uneven formation. However, vision-based fog detection methods typically estimate visibility for individual images and ignore the difference in the characteristics of even and uneven fog, lacking use of temporal information to differentiate between normal foggy weather and agglomerate fog events. Meanwhile, detection of fog at night faces strong interference from car lights that is always overlooked. This study proposes a nighttime agglomerate fog event detection method for videos, taking into account car light interference. Depth disparity feature is constructed based on the information entropy of depth estimation result, and in order to build a metric for uneven characteristics in the field of view, we creatively introduce the Moran’s index to establish uneven feature, generating two-dimensional feature time series for each video. By extracting interpretable features from the two-dimensional feature time series after removing car light interference frames, a classification model based on XGBoost is built to differentiate agglomerate fog, normal fog, and no fog videos. Experiments are carried out utilizing real monitoring data from roadside surveillance cameras to validate the effectiveness of features and model. Furthermore, a fog event detection dataset containing over 1500 videos is established, making up data scarcity for vision-based agglomerate fog event detection and providing support for future research.

Impact of Assimilating Uncrewed Aircraft System Observations on River Valley Fog Prediction

Abstract The impact of assimilating targeted Uncrewed Aircraft System (UAS) observations on the prediction of radiation and river-valley fog is assessed using Observing System Experiments (OSEs). Multi-rotor UAS were deployed during FOGMAP (Frequent in situ Observations Above Ground for Modeling and Advanced Prediction of fog) which took place during the summer of 2022 in northern Kentucky. Targeted UAS missions were flown to sample the spatio-temporal variability of temperature and moisture in the vicinity of the Cincinnati/Northern Kentucky International Airport. During each mission the UAS performed near-continuous profiling at two locations between the surface to 120 m AGL throughout the night. Data denial experiments were performed using the Ensemble Kalman Adjustment Filter available in NSF NCAR’s Data Assimilation Research Testbed (DART) to determine the impact of assimilating UAS observations on the skill of analyses and forecasts issued during potential fog events. Simulations that only assimilated conventional observations tended to have a dry bias in the analyses and forecasts. The dry bias in the analyses was reduced in experiments that assimilated UAS observations leading to improved probabilistic predictions of fog. Sensitivity tests revealed that the ensemble mean analyses were improved when assimilating UAS observations of specific humidity rather than relative humidity (RH) due to the existence of a cold bias near the surface and the negative covariance between RH and temperature. It was also found that either the assumed observation error variance of (1 g kg−1)2 or the ensemble spread of the background specific humidity was too large since their sum tended to overestimate the Root Mean Squared Error (RMSE) of the predicted ensemble mean values.

Robust polarimetric dehazing algorithm based on low-rank approximation and multiple virtual-exposure fusion

Polarimetric dehazing is an effective way to enhance the quality of images captured in foggy weather. However, images of essential polarization parameters are vulnerable to noise, and the brightness of dehazed images is usually unstable due to different environmental illuminations. These two weaknesses reveal that current polarimetric dehazing algorithms are not robust enough to deal with different scenarios. This paper proposes a novel, to our knowledge, and robust polarimetric dehazing algorithm to enhance the quality of hazy images, where a low-rank approximation method is used to obtain low-noise polarization parameter images. Besides, in order to improve the brightness stability of the dehazed image and thus keep the image have more details within the standard dynamic range, this study proposes a multiple virtual-exposure fusion (MVEF) scheme to process the dehazed image (usually having a high dynamic range) obtained through polarimetric dehazing. Comparative experiments show that the proposed dehazing algorithm is robust and effective, which can significantly improve overall quality of hazy images captured under different environments.

Perfect vortex Laguerre-Gauss beams as a carrier in the MMF/FSO communication system

This paper presents a novel, to the best of our knowledge, high-speed transmission system that integrates a new structured light beam, specifically the perfect vortex Laguerre-Gaussian (PVLG) beam, with an optical code division multiple access (OCDMA) system utilizing a premutation vector (PV) code. The PVLG beams are distinguished by their unique shape, which remains nearly invariant during propagation regardless of the azimuthal order of the orbital angular momentum (OAM), facilitating the multiplexing of multiple OAM beams within the same spatial area. Additionally, the system employs hybrid multimode fiber (MMF) and free space optics (FSO) channels, with consideration of foggy weather conditions in the FSO channel. A comparative analysis between the performance of PVLG beams and standard LG beams is conducted. Performance evaluation metrics include the Q-factor, bit error rate (BER), and eye diagrams, providing comprehensive insights into received signal quality. The results demonstrate that the system utilizing PVLG beams outperforms the one using standard LG beams. Specifically, the system achieves a maximum MMF length of 0.35 km with a BER of approximately 10−4 and a Q-factor of around three when the MMF cable channel is used only. For the FSO channel, the achievable ranges are 1.1 km, 0.7 km, and 0.35 km under low fog (LF), medium fog (MF), and high fog (HF) conditions, respectively, maintaining the same BER and Q-factor values. Moreover, the hybrid MMF/FSO channel extends the transmission range to 1.2 km under LF conditions and to 0.45 km under HF conditions, with consistent BER and Q-factor values. Each of the four PVLG beams carries 40 Gbps, resulting in a total transmission capacity of 160 Gbps. Thus, the proposed system is well positioned to meet the high-speed data transmission demands of next-generation 6G networks.

Near miss road traffic accidents and associated factors among truck drivers in Gamo zone, southern Ethiopia by using a contributory factors interaction model.

Road traffic accidents (RTAs) are among the leading causes of injuries, fatalities, and the resulting increase in financial burdens worldwide. Every year, RTAs cause numerous serious injuries and fatalities in Ethiopia. it is important to understand how prevalent near-miss crash accidents are, and which by definition could have injured the victim but did not result in an actual accident. The determinants of these near-misses are essential in road crash accident reduction strategies. In spite of the fact that near-miss accidents are much more common than actual losses or injuries, very little research has been conducted on them. Thus, this study was intended to assess the near-miss accidents and associated factors among truckers in Gamo zone, southern Ethiopia. The community-based cross-sectional study was employed from May 12 to July 10,2022, using a structured interviewer-administered questionnaire. A simple random sampling technique was used to select participants. The data were analyzed using the statistical package for social sciences. A binary and multivariate logistic regression model was used to identify the determinants of near-miss accidents. A statistical significance level was set at p < 0.05. About 72.5% of truckers had experienced near-miss road traffic accidents. The majority of the near-miss accidents were caused by speeding, followed by driving on the wrong side of the road and skidding, 65 (22.6%), 39 (13.5%), and 38 (13.2%), respectively. Driving frequency per week, location of accidents, condition of the road, sleeping status, and weather conditions were significantly associated with near-miss accidents. The prevalence of near-miss accidents is high in the Gamo zone. Being a younger and less educated driver, high driving frequency per week, driving on major roads and junctions, foggy weather, and inadequate sleep all contribute to the occurrence of accidents. Road safety measures that could address these identified factors are required to mitigate potential RTAs.

An Algorithm for Ship Detection in Complex Observation Scenarios Based on Mooring Buoys

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms.

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network.

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase and a simple neural network for the deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to improve tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and costs. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, a speed increase by 37.56%, and identity switches decreased by 46.81%.

DST-DETR: Image Dehazing RT-DETR for Safety Helmet Detection in Foggy Weather.

In foggy weather, outdoor safety helmet detection often suffers from low visibility and unclear objects, hindering optimal detector performance. Moreover, safety helmets typically appear as small objects at construction sites, prone to occlusion and difficult to distinguish from complex backgrounds, further exacerbating the detection challenge. Therefore, the real-time and precise detection of safety helmet usage among construction personnel, particularly in adverse weather conditions such as foggy weather, poses a significant challenge. To address this issue, this paper proposes the DST-DETR, a framework for foggy weather safety helmet detection. The DST-DETR framework comprises a dehazing module, PAOD-Net, and an object detection module, ST-DETR, for joint dehazing and detection. Initially, foggy images are restored within PAOD-Net, enhancing the AOD-Net model by introducing a novel convolutional module, PfConv, guided by the parameter-free average attention module (PfAAM). This module enables more focused attention on crucial features in lightweight models, therefore enhancing performance. Subsequently, the MS-SSIM + ℓ2 loss function is employed to bolster the model's robustness, making it adaptable to scenes with intricate backgrounds and variable fog densities. Next, within the object detection module, the ST-DETR model is designed to address small objects. By refining the RT-DETR model, its capability to detect small objects in low-quality images is enhanced. The core of this approach lies in utilizing the variant ResNet-18 as the backbone to make the network lightweight without sacrificing accuracy, followed by effectively integrating the small-object layer into the improved BiFPN neck structure, resulting in CCFF-BiFPN-P2. Various experiments were conducted to qualitatively and quantitatively compare our method with several state-of-the-art approaches, demonstrating its superiority. The results validate that the DST-DETR algorithm is better suited for foggy safety helmet detection tasks in construction scenarios.

LID-Net: A lightweight image dehazing network for automatic driving vision systems

Visual system provides comprehensive road information for autonomous driving vehicles. Haze adversely affects the quality of driving images captured by onboard cameras, which poses a significant challenge to the safe operation of vehicles relying on pure vision-based autonomous driving solutions. To address the above issues, a lightweight image dehazing algorithm using a multi-scale architecture called LID-Net is proposed. LID-Net consists of Haze Extraction (HE) blocks and Haze Removal (HR) blocks. The HE block captures more haze features by employing a larger receptive field. The HR block introduces different attention weights to different regions to better remove haze of different concentrations. Furthermore, a Brightness Compensation unit is designed to address the issue of reduced brightness in images following dehazing. This unit compensates for the image's brightness without changing the color of the dehazed image. LID-Net is compared with other state-of-the-art methods on two real-world foggy weather datasets. The results indicate that LID-Net outperforms other methods in terms of dehazing effectiveness. LID-Net can efficiently process an image with a resolution of 1280 × 720 at 125 frames per second, which can fully meet the requirements of real-time processing of automatic driving systems.

Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Fast and accurate object detection in foggy weather is crucial for visual tasks such as autonomous driving and video surveillance. Existing methods typically preprocess images with enhancement techniques before the object detector, so that the real-time performance of object detection decreases to some extent. Meanwhile, many popular object detection models rely solely on visual features for localization and classification. When fog is present, visual features would be so adversely impacted that the detection accuracy sharply decreases. Therefore, we propose an end-to-end prior knowledge-guided network called DR-YOLO for object detection in foggy weather. DR-YOLO integrates the atmospheric scattering model and the co-occurrence relation graph as prior knowledge into the entire training process of the detector. Firstly, Restoration Subnet Module (RSM) is designed to employ the atmospheric scattering model to guide the learning direction of the detector for dehazing features. Specifically, it is only adopted during the training process and does not increase the time cost of detection process. Secondly, for guiding the detector to pay more attention to potential co-occurring objects in the same scene, we introduce Relation Reasoning Attention Module (RRAM) that utilizes the co-occurrence relation graph to supplement deficient visual features in foggy weather. In addition, DR-YOLO employs Adaptive Feature Fusion Module (AFFM) to effectively merge the key features from the backbone and neck for the needs of RRAM and RSM. Finally, we conduct experiments on clear, synthetic and real-world foggy datasets to demonstrate the effectiveness of DR-YOLO. The source code is available at https://github.com/wenxinss/DR-YOLO.