Computer Vision Applications Research Articles

No tricks no bluff, focusing on localizing crisp boundaries in image media

Boundary detection, as a fundamental task for computer vision applications, plays an important role in many tasks such as image deblurring, semantic segmentation, camouflaged object detection, and salient object detection. The thickness problem of boundary prediction is so prevalent in existing methods that NMS has to be used as a post-processing method to thin the prediction boundaries. However, we believe that additional post-processing does not constitute an end-to-end behavior, and the results obtained do not reflect the true boundary localization capabilities of the algorithm. In comparison to the superior end-to-end approach, NMS reveals notable limitations in rectifying erroneous predictions and achieving high-quality results similar to the ground truth. Furthermore, the use of NMS will inevitably compromise the prediction of fragile boundaries. Therefore, we remove the NMS step from the boundary detection process and generalize two reasons for the detector’s poor ability to localize crisp boundaries: boundary blurring and static cues. Aliasing region loss (Lar) and hollow region loss (Lhr) are introduced to mitigate the occurrence of false positives in various types of regions, thereby addressing the issue of boundary blurring. For static cues, we propose a dimension attention (DimAttn) module which can realize multidimensional and multi-range clew interaction. In fact, our method can be plug-and-play in any existing boundary detection model. Extensive experiments have demonstrated that the boundary prediction results of the proposed framework are crisper and exhibit remarkable superiority in detecting fragile boundaries compared with scenarios utilizing NMS.

Artificial intelligence and robotic surgical education

There are numerous barriers in robotic surgical training, including reliance on observational learning, low-quality feedback, and inconsistent assessment. Artificial intelligence (AI) offers potential solutions to these central problems in robotic surgical education and may allow for more efficient and efficacious training. Three key areas in which AI has particular relevance to robotic surgical education are video labeling, feedback, and assessment. Video labeling refers to the automated designation of prespecified categories to operative videos. Numerous prior studies have applied AI for video labeling, particularly for retrospective educational review after an operation. Video labeling allows learners and their instructors to rapidly identify critical parts of an operative video. We recommend incorporating AI-based video labeling into robotic surgical education where available. AI also offers a mechanism by which reliable feedback can be provided in robotic surgery. Feedback through AI harnesses automated performance metrics (APMs) and natural language processing (NLP) to provide actionable and descriptive plans to learners while reducing faculty assessment burden. We recommend combining supervised AI-generated, APM-based feedback with expert-based feedback to allow surgeons and trainees to reflect on metrics like bimanual dexterity and efficiency. Finally, summative assessment by AI could allow for automated appraisal of surgeons or surgical trainees. However, AI-based assessment remains limited by concerns around bias and opaque processes. Several studies have applied computer vision to compare AI-based assessment with expert-completed rating scales, though such work remains investigational. At this time, we recommend against the use of AI for summative assessment pending additional validity evidence. Overall, AI offers solutions and promising future directions by which to address multiple educational challenges in robotic surgery. Through advances in video labeling, feedback, and assessment, AI has demonstrated ways by which to increase the efficiency and efficacy of robotic surgical education.

Unsupervised Domain Adaptation Depth Estimation Based on Self-attention Mechanism and Edge Consistency Constraints

In the unsupervised domain adaptation (UDA) (Akada et al. Self-supervised learning of domain invariant features for depth estimation, in: 2022 IEEE/CVF winter conference on applications of computer vision (WACV), pp 3377–3387 (2022). 10.1109/WACV51458.2022.00107) depth estimation task, a new adaptive approach is to use the bidirectional transformation network to transfer the style between the target and source domain inputs, and then train the depth estimation network in their respective domains. However, the domain adaptation process and the style transfer may result in defects and biases, often leading to depth holes and instance edge depth missing in the target domain’s depth output. To address these issues, We propose a training network that has been improved in terms of model structure and supervision constraints. First, we introduce a edge-guided self-attention mechanism in the task network of each domain to enhance the network’s attention to high-frequency edge features, maintain clear boundaries and fill in missing areas of depth. Furthermore, we utilize an edge detection algorithm to extract edge features from the input of the target domain. Then we establish edge consistency constraints between inter-domain entities in order to narrow the gap between domains and make domain-to-domain transfers easier. Our experimental demonstrate that our proposed method effectively solve the aforementioned problem, resulting in a higher quality depth map and outperforming existing state-of-the-art methods.

Implementation of Vehicle Detection and Tracking Model Using YOLO and DeepSORT for Controlling Traffic Rule Violation

The field of computer vision has advanced significantly in the last several years, especially in the areas of object tracking and detection. This research offers a thorough analysis of the use of a vehicle tracking and detection model that makes use of DeepSORT (Deep Simple Online and Realtime Tracking) for tracking and YOLOv8 (You Only Look Once version 8) for detection. By combining these state-of-the-art methods, it will be possible to track and identify vehicles in a variety of situations in real-time, improving surveillance and traffic management as well as autonomous vehicle navigation. This project intends to develop computer vision applications in the transportation and surveillance sectors by means of a thorough examination and experimentation.

A prior knowledge-guided distributionally robust optimization-based adversarial training strategy for medical image classification

Medical image classification plays a vital role in computer vision applications within the field of healthcare and medicine, and deep neural network-based classifiers are continuously achieving new breakthroughs and demonstrating tremendous potential in medical imaging analysis. However, the lack of robustness in deep learning techniques makes it risky to apply these classifiers to the domain of healthcare. In addition, the existing adversarial training strategies and domain generation methods are difficult to generalize into the medical imaging field challenged by complex medical texture features. To address this issue, we propose a medical morphological knowledge-guided adversarial training strategy, by jointly considering the robustness against medical data distribution shifts and adversarial attacks from the view of distributionally robust optimization. First, we train a surrogate model with the augmented dataset by guided filtering for capturing model attention on medical morphological information. Next, we design a gradient normalization-based prior knowledge injection module to transfer the attention information learned by surrogate model to the main classifier. Finally, we design a distributionally robust a optimization-based training strategy to induce the main classifier to learn key diagnostic clues as well as enhance the robustness against adversarial attacks. To evaluate the effectiveness of the proposed methods, we perform experiments on two types of in-domain and out-of-domain medical image sets, which contain lung CT scan datasets and dermatoscopic image datasets. Comparative results show that the proposed training strategy achieves higher adversarial attack accuracy than all involved state-of-the-art adversarial training methods and domain generation methods. The code is available at https://github.com/sysu19351146/MMK-DRO.

Maritime distress target detection algorithm based on YOLOv5s‐EFOE network

AbstractTraditional maritime search and rescue methods mainly rely on manual operation, which takes a long time to identify and results in poor search and rescue results. This paper applies computer vision technology to the field of maritime distress object detection and proposes an improved object detection algorithm YOLOv5s‐EFOE based on the YOLOv5s algorithm. Firstly, the authors change the detection head of the YOLOv5s algorithm to use a mixed channel strategy to build a more efficient decoupling head, reducing the number of 3 × 3 convolutional layers in the middle of the decoupling head to only one. The width of the head is scaled by the width multipliers of Backbone and Neck. Secondly, in conjunction with the SimOTA matching algorithm, the positive samples of the target to be tested are dynamically allocated to improve the recognition ability of different targets. Finally, considering the low pixel value of the maritime distress target from the perspective of unmanned aerial vehicles (UAV), the loss function CIoU in the original YOLOv5s is changed to EIoU. EIoU not only considers the distance and aspect ratio of the centre point, but also considers the true difference between the width and height of the prediction box and the real box, which improves the prediction accuracy of the anchor box. Experiments are conducted on a subset of the public dataset SeaDronesSee. The of the YOLOv5s‐EFOE model proposed in this paper reached 75.9%, reached 79.9%, and reached 44.5%. These indicators are superior to the original YOLOv5s model, YOLOv7 series models, and YOLOv8 series models. Compared with the original model, the YOLOv5s‐EFOE model has increased the by 5.4%, by 5.6%, and by 4.6%, improving the difficult to detect and missed detection situations. This model can be deployed on UAVs and can effectively identify maritime distress target, achieving the purpose of search and rescue.

Smartphone Camera for Angiographic Computer Vision in Vascular Medicine

Aim: This study aimed to develop a TensorFlow Lite algorithm for angiography classification and to deploy it on a basic mobile smartphone device, thereby verifying the proof of concept for creating a comprehensive end-to-end mobile computer vision application for vascular medicine. Materials and Methods: After ethical approval by the local ethics committee, we collected institutional and open source peripheral angiograms of lower limbs. The angiograms were labeled by a researcher with more than 10 years of experience in vascular surgery. The labeling included dividing the angiograms according to their anatomical pattern into the Global Limb Anatomic Staging System (GLASS). The model was developed using the open-source TensorFlow framework for general image classification and deployed as an Android application. Results: The model utilized 700 angiograms, distributed as follows within the femoropoliteal GLASS disease (fp) categories: fp0 – 187 images, fp1 – 136 images, fp2 – 128 images, fp3 – 97 images, fp4 – 152 images. The reference dataset included 372 non-angiographic images (not_angio). Consequently, the entire model included 1,072 images. After training and deployment, the model demonstrated the following performance: a mean accuracy of 0.72. The best self-reported accuracy per class was for fp0 0.72, fp4 0.83 and not_angio 1.0 classes. Conclusion: We discovered that a smartphone camera could be utilized for angiographic computer vision through end-to-end applications accessible to every healthcare professional. However, the predictive abilities of the model are limited and require improvement. The development of a robust angiographic computer vision smartphone application should incorporate an upload function, undergo validation through head-to-head human-machine comparisons, potentially include segmentation, and feature a prospective design with explicit consent for using collected data in the development of AI models.

Impact of parity on prepartum activities and behaviour in dromedary camels under farm conditions.

This study was conducted to assess the disparities in camel activities such as eating, drinking, sitting, standing, and sleeping between primiparous and multiparous females before parturition using computer vision. Also, any extraordinary behaviours during the final 2 h before parturition and the necessary manual interventions were meticulously recorded. Five primiparous (age: 4.5-7 years) and 7 multiparous (age: 8-14 years; parity: 2.1 ± 1.5) dromedary camels, were included in this study. Pre-partum females were housed double in a parturition pen provided with two Reolink RLC-810A cameras and the data were collected and recorded for each female. Two primiparous and 1 multiparous female required assistance in pulling the calf from both forelimbs to complete their parturition (27.3%). The drinking and sleeping activities were similar in primiparous and multiparous females during the recorded 32 h leading up to calving. Only eating activity exhibited a longer period in primiparous females compared to multiparous females specifically during the 12-h before calving. Sitting activity was longer, and standing activity was shorter in multiparous than in primiparous females during the 24, 12, and 6 h before calving. All parturient camels, whether primiparous or multiparous, exhibited signs of distress. Some extraordinary behaviours were observed, such as two multiparous females attempting to deter their primiparous counterparts from eating. Additionally, three females displayed a distinctive standing position on their knees while their hind limbs were in a complete standing position for 3-5 min before transitioning to sitting or standing positions. Furthermore, one primiparous female stood while the head and forelimbs of the calf partially protruded from her vulva. In conclusion, the application of computer vision and deep learning technology proves valuable for observing prepartum camels under farm conditions, potentially reducing economic losses stemming from delayed human intervention in dystocia cases.

Reducing Road Mishaps: A CNN Model for Driver Fatigue Detection

Over the last few decades, there have been substantial developments in a variety of domains, including computer science, artificial intelligence, and machine learning, which has accelerated the evolution and application of intelligent systems in specific areas such as transportation. One way these systems are used in transportation is through fatigue detection, and to enable this technology, Convolutional Neural Networks serve as the foundation on which this system is built. CNNs are frequently employed in computer vision applications because they can automatically extract pertinent features from input data without the need for manual feature engineering. This CNN-based fatigue detection system was built in-order to address the repercussions of fatigued driving by monitoring the driver’s facial features in real-time so as to predict the fatigue levels and send out an alert to restore the driver’s alertness level so as to reduce road mishap. The system relies on visual input from a camera, which sends this input to a fatigue detection program installed on a Raspberry Pi microprocessor and sends out via alerts via a display and audio alerts via a piezoelectric buzzer. The model shows excellent performance, consistently achieving high accuracy, peaking at 97.34% on the 64th epoch, and consistently maintaining high validation accuracy, reaching 96.02% in the 67th epoch, indicating its ability to function effectively.

Computer vision to enhance healthcare domain: An overview of features, implementation, and opportunities

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one of the growing modern AI technologies. The distribution and administration of medications are about to change by using CV for medication management. This system scans pharmaceutical labels and keeps track of the process from delivery to administration using cameras, sensors, and computer algorithms. In order to assure accuracy in medicine delivery and dose, the system also makes it easier for doctors, nurses, and chemists to communicate. The computer vision-driven medication management system can significantly lower the number of medical mistakes that result from inaccurate or missing prescriptions, improper doses, or simply forgetting to take a particular drug. An exhaustive literature review has been done to identify work related to the research objectives. This paper is about CV and their need in healthcare. Various tasks associated with CV in the healthcare domain are discussed. Targeted healthcare goals through CV traits are briefed. Finally, the significant applications of CVs in healthcare were identified and discussed. Nowadays, CV has practical uses in healthcare. Its methods are widely used since they have shown excellent utility in several medical contexts, including medical imaging and surgical planning. The CV is used to study how to program computers to comprehend digital pictures. Numerous medical applications utilise this technology, such as automated abnormality identification, illness diagnosis, and surgical procedure guiding. CV is expanding quickly and has enormous promise to enhance healthcare. Some of the many CV applications in the healthcare sector include patient identification systems, medical picture analysis, surgical simulation and illness diagnosis.

Defect identification of electricity transmission line insulators based on the improved lightweight network model with computer vision assistance

This work aims to ensure the safe operation of electricity transmission lines and reduce costs and maintenance difficulties. It studies the application of computer vision (CV) in the defect identification of electricity transmission lines. In addition, this work proposes a method to improve the lightweight network model to provide an effective identification model to solve the problem of electricity transmission line defects. Firstly, GraphCut segmentation and Laplace algorithms are employed to expand and sharpen the electricity transmission line image. Secondly, in light of the Depth Separable Convolution algorithm, a defect detection model for the electricity transmission line insulator is proposed based on the You Only Look Once 4 (YOLOv4) network. Moreover, MobileNetV1 is utilized to improve this lightweight network model. Finally, this work uses ImageNet, a large public dataset, to validate the proposed model experimentally. The research results reveal that: (1) In the model testing results, all research indicators of the model are greater than 90 %, indicating an excellent detection accuracy of this model. (2) The improved YOLOv4 model can increase the detection speed to 53 frames/s at the cost of 2.4 % accuracy. (3) After image sharpening, the improved YOLOv4 model has promoted the insulator defects' detection ability to a certain extent. The above outcomes suggest that the improved YOLOv4 model can predict more efficiently and accurately and reduce unnecessary false positives. This illustrates that the proposed model is feasible and is expected to be applied to the defect identification of electricity transmission lines in practice. These findings fully demonstrate this work's vital value in enhancing the prediction efficiency and accuracy, thus offering a strong preference for the defect identification of electricity transmission lines in practical applications.

The wicked problem of naming the intangible: Abstract concepts, binary thinking, and computer vision labels

AbstractDelving into the intricate complexities of naming and categorizing the visual evocation of abstract concepts, this paper brings to light the limitations of relying on binary thinking to tackle these inherently intricate “wicked problems.” As computer vision applications rapidly expand, the pressing challenge of accurately labeling these abstract concepts in visual media comes into focus, necessitating a close examination of the interplay between visual data, nuanced cultural meanings, and artificial intelligence (AI). This work discusses the role these concepts play in automatic visual indexing, as well as the ways in which they expose how binary frameworks curtail technical performance and perpetuate power dynamics. To address this, the paper draws upon insights from recent cognitive neuroscience research and advocates for a more comprehensive, queer, and situated understanding of these concepts. This approach highlights the significance of humanistic and ethical perspectives in shaping the trajectory of AI development.

Efficient tick-shape networks of full-residual point-depth-point blocks for image classification

Light-weight convolutional neural networks (CNNs) are crucial for deploying computer vision applications in mobile devices thanks to their compact models, small computational complexity, and energy efficiency. However, such models ordinarily have steady-increased channels in their backbone leading to a sharp increase in the model size; while the deficiency of identity mappings in their residual mechanism can lead to modest performance in feature extraction. To mitigate those issues, we propose light-weight networks based on three novel concepts as follows. Firstly, an efficient perceptron is presented to encapsulate point-depth-point (PDP) features extracted by light-weight convolutions along with a full-residual (FR) mechanism through the architecture of a network. This full-residual connection is proposed to deal with the shortcoming of the existing light-weight models whose architecture has incompletely exploited identity mappings. It is due to the variability of spatial dimension caused by several strides of their convolutional operations. Secondly, a tick-shape backbone is then introduced by designing its structure in accordance with the channel elasticity of the FR-PDP perceptron subject to the shape of a check mark. Thirdly, taking advantage of the channel elasticity concept, three tick-shape networks (TickNets) are constructed in a light-weight architecture by hooking one or more tick-shape backbones. Experimental results for image classification on benchmark datasets have clearly corroborated the efficacy of the proposed methods. The code implementation of tick-shape models is available at https://github.com/nttbdrk25/TickNets.

Yoga Posture Recognition Through Computer Vision and Deep Learning

Abstract: India is the place where yoga's science and discipline originated 5,000 years ago. The development of it in ancient India occurred through the Indus-Sarasvati civilization. The Indian subcontinent is where this ancient practice originates,and it has been cherished for its multifaceted benefits that encompass physical, mental, and spiritual. Through asana, meditation, and other breathing techniques, harmony is brought to both body and mind. It also brings peace to the mind. The rise in stress in today's modern lifestyle has led to a global growth in the popularity of yoga. The goal of yoga is to mechanically align the body with effort on the muscles, ligaments, and joints to achieve optimal posture. If the asanas are not performed properly, strain in the joints, ligaments, and backbone can occur, which can have an impact on the hip joints. Therefore, it is vital to maintain correct yoga postures while performing various asanas. The availability of yoga posture prediction and automatic movement analysis is now possible due to the development of computer vision algorithms and sensors. In this work, a framework for recognizing a yoga posture from an image has been built using deep learning techniques such as convolutional neural networks (CNN) and machine learning (ML). The CNN layer is used to extract characteristics from the keypoints, and it is succeeded by a large short-term recollection that recognizes the sequence in which a set of frames occurs in order to make predictions. The stances are then categorized as either correct or inappropriate. The system will provide the user with feedback and display the correctness of posture if the right attitude is detected. This review paper synthesizes the current state-of-the-art in the field of yogic intelligence, focusing on the application of computer vision and deep learning techniques for recognizing yoga postures

Visual place recognition for autonomous mobile robot navigation using LoFTR and MAGSAC++

Autonomous mobile robots are defined as robotic entities capable of independent movement and intelligent decision-making, relying on their ability to perceive and analyze their surroundings, including objects in their environment. In Simultaneous Localization and Mapping (SLAM) systems, loop closure is often achieved through visual place recognition techniques, where the system compares the current visual input with previously observed scenes to identify matches. In computer vision applications, Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) are popular feature extraction algorithms used for such as key point detection, matching, and image registration tasks. The choice of inlier threshold should be based on the specific characteristics of the application and the nature of the images being processed. It often requires experimentation and tuning to find the optimal balance between robustness and accuracy. It Utilizes the pre-trained Local Feature Transformer (LoFTR) and MAGSAC++ estimator to address these drawbacks by employing the number of inliers to determine the similarity between two images for visual place recognition. Our experiment demonstrates that the number of inliers can determine the similarity of locations between two images. Scale variations and translation in location significantly influence the resulting number of inliers. Comparing images from the same location and from different locations yields varying numbers of inliers. The number of inliers significantly influences the similarity of locations. At the same location, the number of inliers is above 150, while at different locations, the number is below 150.

Sign Language Recognition using CNN

Abstract: The sign language Recognition System is a technology that bridges the gap between the deaf people and the hearing world. Sign language is a vital mode of expression for deaf people, yet effective communication remains a challenge. This project aims to develop a robust Sign Language Recognition System capable of accurately translating sign language gestures into text. The system utilizes a deep learning approach, specifically convolutional neural networks (CNNs) to analyze video input of sign language and then generate the corresponding output. The project involves data collection, pre-processing, segmentation, feature extraction, model training, and evaluation. The proposed SLR system has the potential to enhance. Communication and accessibility for deaf individuals, promoting inclusivity and improving their quality of life. The project represents a comprehensive exploration of both technical and ethical aspects in the realm of computer vision and deep learning applications.

Sub-Band Backdoor Attack in Remote Sensing Imagery

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery.

Fourier Domain Adaptation for the Identification of Grape Leaf Diseases

With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model.

Analyzing the Impact of Loss Functions on Dehazing Effectiveness and Unveiling the Discrepancy between Quantitative and Qualitative Results

The work focuses on the analysis of the impact of loss functions on the effectiveness of a model for dehazing images. Dehazing, the process of removing haze or atmospheric scattering from images, plays a crucial role in various computer vision applications. To enhance the performance of dehazing models, it is essential to examine different loss functions and their variations. In this study, we employ a Generative Adversarial Network (GAN) as our model and evaluate the performance of various loss functions. The primary objective is to assess how well each loss function is capable of dehazing an image, while specifically investigating the influence of various structural similarity index (SSIM) loss variations on the dehazing effectiveness. Our experimental results reveal a notable discrepancy between qualitative and quantitative outcomes. Contradicting the traditional interpretation in literature, our qualitative analysis reveals that the SSIM IQA metric may not be a fully reliable indicator of dehazing effectiveness despite it being viewed to be correlated to human visual perception unlike Mean Square Error and Peak Signal to Noise Ratio Metrics. Moreover, we demonstrate that relying solely on quantitative results may lead to the selection of an inappropriate loss function. This finding emphasizes the significance of qualitative analysis in evaluating the performance of dehazing models. The disparity between quantitative and qualitative results emphasizes the need for newer image assessment metrics in the domain that can effectively bridge this gap. Such metrics should be able to be better correlated with human perception. This research contributes to the field of image dehazing by shedding light on the importance of qualitative analysis in addition to quantitative evaluation. By comprehensively analysing the impact of variations of SSIM loss functions and their combination with Mean Absolute Error (MAE) loss, we provide valuable insights into enhancing the effectiveness of dehazing models.

Deep Learning in Computer Vision: A Critical Review

Today, a machine learning tool has gained a lot of consideration as a data analysis and image processing tool, with encouraging results. Massive amounts of data are being generated in every field or any domain at a distressing rate. This review paper provides a brief overview of current technologies and a conceptual explanation of the development of computer vision with image processing and the usage of specific regions in their respective fields. Computer vision with deep learning allows researchers to investigate pictures and videos to attain essential facts, recognize facts on occasions, or provide explanations and exquisite patterns. It utilized the approach of diverse-variety utility areas with large-scale data mining. We employed a review to extract and synthesize the techniques and attributes that have been used in different applications of computer vision, like image classification, image localization, object detection, and many more, with their features. So according to computer vision tasks, there are Deep Belief Networks, Convolutional Neural Networks (CNN), Deep Boltzmann Machines (DBN), Recurrent Neural Networks (RNN), Stacked Denoising Auto Encoders, and Long-Short Term Memory (LSTM) algorithms that are some of the most important advanced machine learning (deep learning) algorithms. Lastly, a quick evaluation of future approaches to constructing advanced machine learning algorithms is provided for challenges and issues related to computer vision with improved performance.