Car Damage Research Articles

An Insurtech Platform to Support Claim Management Through the Automatic Detection and Estimation of Car Damage from Pictures

Claims management is a complex process through which an insurance company or responsible entity addresses and handles compensation requests from policyholders who have suffered damage or losses. This process entails several stages, including the notification of the claim, damage assessment, settlement of compensation, and, if necessary, dispute resolution. Fair, transparent and timely claims management is crucial for maintaining policyholders’ trust while also limiting the financial impact on the insurer. Technological innovations, such as the use of artificial intelligence and automation, are positively influencing this sector, enabling faster and more effective claims management. This study reports on Insoore AI, an insurtech solution that aims to automate a portion of claims management by integrating a computer vision solution based on some latest developments in deep learning to automatically recognize and localize car damage from user-provided pictures.

Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case

Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer’s images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals.

Influence of Road Infrastructure Design over the Traffic Accidents: A Simulated Case Study

The influence of road infrastructure over the severity of road accidents, in particular some specific features of it, represents the subject of this study. Generally, when an accident occurs, its causes are represented by a number of factors such as driver experience, fatigue, driving under the influence of alcohol and other psychoactive substances, road configuration, weather conditions, speeding, distracted driving, and unsafe road infrastructure. Road design is a key factor regarding the safety of all traffic participants. In this paper, the influence of unsafe roadside element designs on the incidence of traffic accidents, the degree of vehicle passenger injury, and the level of car damage were investigated. The present study was inspired by the high number of accidents produced on European route E68 (DN1) in Romania, a significant part of which was generated and accentuated by the effects of improper roadside design.

Automated vehicle damage classification using the three-quarter view car damage dataset and deep learning approaches

Automated procedures for classifying vehicle damage are critical in industries requiring extensive vehicle management. Despite substantial research demands, challenges in the field of vehicle damage classification persist due to the scarcity of public datasets and the complexity of constructing datasets. In response to these challenges, we introduce a Three-Quarter View Car Damage Dataset (TQVCD dataset), emphasizing simplicity in labeling, data accessibility, and rich information inherent in three-quarter views. The TQVCD dataset distinguishes class by vehicle orientation (front or rear) and type of damage while maintaining a three-quarter view. We evaluate performance using five prevalent pre-trained deep learning architectures—ResNet-50, DenseNet-160, EfficientNet-B0, MobileNet-V2, and ViT—employing a suite of binary classification models. To enhance classification robustness, we implement a model ensemble method to effectively mitigate individual model dependencies' deviations. Additionally, we interview three experts from the used-car platform to validate the necessity of a vehicle damage classification model using the corresponding dataset from an industrial perspective. Empirical findings underscore the dataset's comprehensive coverage of vehicle perspectives, facilitating efficient data collection and damage classification while minimizing labor-intensive labeling efforts.

Powering AI-driven car damage identification based on VeHIDE dataset

ABSTRACT In the realm of automobile insurance, the imperative of automating car damage evaluation has surged, offering streamlined assessment processes and heightened accuracy. Deep learning techniques have notably influenced vehicle damage assessment, reshaping insurance procedures. However, the primary challenge remains in crafting robust models for damage detection and segmentation. This study presents a novel contribution through the development of the Vehicle Damage Detection (VehiDE) dataset, specifically tailored for comprehensive car damage assessment. The dataset, encompassing 13,945 high-resolution images annotated across eight damage categories, serves as a foundational resource for advancing automated damage identification methodologies. Notably, VehiDE offers support for multiple tasks, including classification, object detection, instance segmentation, and salient object detection, thereby fostering versatile research avenues. Through extensive experimental analysis, including the evaluation of state-of-the-art methodologies on VehiDE, this study not only highlights the dataset's efficacy but also unveils new insights into the challenging nature of car damage assessment. Moreover, the study pioneers the exploration of salient object detection techniques in this domain, showcasing their potential in addressing irregular damage types. By offering VehiDE to the research community, we aim to catalyze advancements in the field of car damage assessment, paving the way for more accurate and efficient automated systems.

Implementation of Car Damage Detection in Insurance Claims

The automotive industry is witnessing a growing demand for advanced safety and maintenance systems to enhance vehicle safety and reduce maintenance costs. Car damage detection plays a pivotal role in achieving these objectives. This abstract provides an overview of the concept of car damage detection, its importance, and the technologies and methodologies involved. Car damage detection refers to the process of identifying and assessing physical damage to a vehicle, such as dents, scratches, cracks, or structural deformities. It is essential for various stakeholders, including car owners, insurance companies, and automotive service providers, as it enables timely repair and maintenance, facilitates insurance claims, and ensures road safety. Key Words: CNN, Mask R-CNN, Image Analysis, Computer Vision, Machine Learning, Deep-Learning.

INSURANCE AMOUNT PREDICTION BASED ON ACCIDENTAL CAR DAMAGE LEVEL USING CNN

There is a paradigm shift in the automotive industry with the advancement of technology, especially in the area of ​​insurance claims handling. In this study, we propose a new method for predicting insurance claims based on car damage levels, utilizing advanced artificial intelligence algorithms for image analysis, particularly employing Convolutional Neural Networks (CNN) for image processing and classification. Our strategy is to provide car damage estimates through picture analysis, making insurance claims in the automobile sector simpler. In order to facilitate user interaction with Flask and increase the predictability of our models, we have also implemented enhancements to the user interface. By combining image analysis with a user-friendly interface, our method offers a practical solution for insurance companies to expedite claim processing, reduce fraudulent activities, and ensure fair settlements for policyholders. This research contributes to the ongoing transformation of insurance claim processing within the automotive industry, highlighting the potential of merging image analysis methodologies with user interfaces for enhanced efficiency and usability. Keywords: Insurance claim prediction, Convolutional Neural Networks (CNNs), Image analysis, User interface, Flask, Automotive industry.

Car Damage Detection with VGG-16

In today's traffic accidents, assessing the damage and keeping damage records quickly is necessary. It is crucial to accelerate damage assessment studies to prevent traffic congestion caused by accidents and open the road to traffic quickly. Deep learning technologies provide various advantages in calculating the magnitude of the damage, displaying the damage situation, and making inferences about the material extent of the damage. In this study, a decision support system is aimed not only for insurance companies or official institutions to see the results but also for the end user and to reveal the damage class of the accidents. The software offered aims to provide an objective perspective not only in accident processes but also in quickly determining the financial value of the vehicle when buying and selling the vehicle. In this study, the training phase of our VGG16-based model, a CNN sub-model, was developed on the data set we obtained from the Kaggle platform (5757 images). With VGG16, our vehicle object detection rate is 98%, the accuracy rate of whether the vehicle is damaged is 90%, the results obtained in the training in which we detect the damaged area is 70%, and finally, the accuracy rate in determining the level of damage (low, medium and high) is 66% has been obtained.

CAR DAMAGE DETECTION USING CNN

In today's modern society, automobiles play a crucial role, and the automatic classification of car damages holds particular significance for the auto insurance industry. Our proposed solution involves the implementation of two Convolutional Neural Network (CNN) models. Specifically, the VGG16 model is employed to identify and assess the location and severity of car damage, while the Mask R-CNN is utilized to accurately mask the damaged regions. Both models collectively provide valuable insights into the extent. The CNN models effectively filter out images without damages, allowing only those with identified damage to be passed on to the object detection model. This strategic approach enhances the overall performance of the model. The core objective of this research project is to achieve maximum accuracy through the utilization of CNN models. TensorFlow, Key Words: E-commerce, Car Damage, Detection, Classification, VGG, Mask RCNN, Severity, Location, Masking

Information extraction from automotive reports for ontology population

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.

Advancements in Computer Vision for Car Damage Detection and Assessment: A Comprehensive Study

Advancements in Computer Vision for Car Damage Detection and Assessment: A Comprehensive Study

ISpot: An intelligent real-time pothole spot identification model using a CNN algorithm

Maintaining roads is a very intricate and significant global concern. Detecting road abnormalities, including potholes, is crucial in road monitoring and management. Identifying potholes is essential to minimize road accidents and car damage and improve travel comfort. Authorities have long seen road maintenance as a significant concern. However, the absence of accurate identification and connecting of road potholes exacerbates the problem. An end-to-end system named Intelligent Spotting (iSpot) of Pathole has been developed to address this issue by providing real-time identification, tracking, and geographical mapping of potholes around the city. A Convolutional Neural Network (CNN) framework is suggested and assessed using a real-world dataset for detecting potholes. Real-time maps displaying pothole locations are created using the Google Maps Application Programming Interface (API). Both pothole identification and mapping are combined into an Android application to offer a comprehensive service via this technology. The suggested model outperforms the baseline techniques regarding accuracy, precision, and F score.

Analysis Performance of One-Stage and Two Stage Object Detection Method for Car Damage Detection

Analysis Performance of One-Stage and Two Stage Object Detection Method for Car Damage Detection

Using Deep Learning and Accident Report Analysis to Determine Culpability and Detect Car Damage

Using Deep Learning and Accident Report Analysis to Determine Culpability and Detect Car Damage

Assessment of Intelligent Vehicle Damage and Cost Estimator for Insurance Companies

Now a day’s many accidents are taking place, Suppose if an accident had taken place and they want to claim with the respective car insurer they need to likely go through the process of having a person inspect the car's damage at your home or a repair centre and then needing to wait for an official estimate and approval to go ahead with getting the necessary repair work done. While this traditional process can be lengthy and keep your car off the road longer than you'd like, artificial intelligence can now play a role in speeding up the process by taking over the appraiser's work. Vehicle damage detection is used to reduce claims leakage during insurance processing. Visual inception and validation are usually done. As it takes a long time because a person needs to come and inspect the damage. Here we are trying to automate the procedure. Using this automation, we can avoid time conception for the insurance claim procedure. In this we are using the VGG16 model to predict the damage of vehicle. We get the accuracy of 94.56% of VGG16.

CPW-DICE: a novel center and pixel-based weighting for damage segmentation

Reliable evaluation of damage in vehicles is a primary concern in the insurance industry. Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm. During the assessment, precise damage segmentation plays a crucial role. Dent is a type of damage that can commonly occur in vehicles. It is difficult to pinpoint and tends to blend in with the background. This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims. Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting. The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations. CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks. Simultaneously, the weight mask is incorporated into DICE loss. Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss. Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation.

CarDD: A New Dataset for Vision-Based Car Damage Detection

Automatic car damage detection has attracted significant attention in the car insurance business. However, due to the lack of high-quality and publicly available datasets, we can hardly learn a feasible model for car damage detection. To this end, we contribute with Car Damage Detection (CarDD), the first public large-scale dataset designed for vision-based car damage detection and segmentation. Our CarDD contains 4,000 high-resolution car damage images with over 9,000 well-annotated instances of six damage categories. We detail the image collection, selection, and annotation processes, and present a statistical dataset analysis. Furthermore, we conduct extensive experiments on CarDD with state-of-the-art deep methods for different tasks and provide comprehensive analyses to highlight the specialty of car damage detection. CarDD dataset and the source code are available at https://cardd-ustc.github.io.

Pothole Detection Using Deep Learning

Particularly in poor nations, potholes are a nuisance that frequently cause car damage or danger to the people inside of moving vehicles. The potholes are holes on roadway surface that can cause damage to vehicles which can cause accidents. Poor road conditions are not just a public nuisance because that causes discomfort to drivers, passengers, as well as damage to vehicles which leads to accidents. The pothole detection project helps drivers avoid potholes on a given route by providing advance alerts of their presence. Create a piece of software that can locate potholes is the plan. The concept may be expanded to create automobiles that can recognize road abnormalities like humps. The program may be used to efficiently address the issue of rising accidents brought on by potholes. And also can be synchronized with GPS or NAVIC to create a real time inter-vehicular alerting system.

Design of a novel masking-based deep convolutional neural network approach for accident classification

This work presents a novel multi-class detection approach for predicting car damage due to accidents or other reasons. The proposed prediction model intends to extract the class information to analyze the objects over the frames. Here, a novel masking-based deep convolutional neural network (MD-CNN) is proposed to capture the car parts’ regions and the classification process. The proposed model works well compared to the various existing approaches. The computation is initiated with the acquisition of damaged images, and pre-processing is performed for data labeling, which is further divided into training and testing samples. The features are internally analyzed by the proposed MD-CNN model, where the internal feature extraction process is a significant advantage with the classification process. The simulation is done in MATLAB 2020a environment where various performance metrics like accuracy, precision, [Formula: see text]-measure, recall and detection rate are evaluated and compared with conventional learning approaches. The model gives an average precision of 94.5% and [Formula: see text]1-score of 91% which is higher than other approaches. The proposed model establishes a better trade-off among the prevailing approaches with superior prediction accuracy.

Real-Time Instance Segmentation Models for Identification of Vehicle Parts

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U = . 50 and A P I o U = . 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively.