Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification

P. Darney
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Abstract

Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.
基于混合深度学习多标签分类的汽车损伤自动检测
自动化基于图像的汽车保险索赔处理是一个重要的机会。本研究解决了混合卷积神经网络辅助下的汽车损伤分类问题,并采用了基于深度学习的分类策略。保险公司可以利用本文设计和实现的汽车损坏分类/检测管道来简化汽车保险索赔政策。由于人工智能领域的最新进步,使用深度卷积网络检测汽车损坏现在成为可能,主要是由于混合转换深度学习算法的计算时间更少,精度更高。本文提出了多类分类方法,将破碎的前照灯/尾灯、玻璃碎片、发动机罩等汽车损坏部件进行分类,并将其汇编到所提出的数据集中。由于数据集的大小有限,该模型已经在广泛的基准数据集上进行了预训练,以最大限度地减少过拟合并了解数据集的更多常见属性。为了提高所提模型的整体性能,利用coco汽车损伤检测数据集对CNN特征提取模型进行Resnet架构训练,准确率达到了90.82%,大大优于之前在可比测试集上的结果。
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