Automobile insurance companies are seeing huge increase in claims from hail-storm damages for more than 10 years now. As the hail damage claims rise, the National Insurance Crime Bureau warns of fraud. So there is an urgent need from insurance companies to deploy a solution which can detect fraud and optimize insurance claims during the hail-storm season.
To meet the above requirement, develop and deploy an advanced NN model to classify dents caused by hail-storm along with body panel for different automobile makes and models. Some of the challenges are given below :
Data Capture Method
The semantic properties of pixel in dents can change due to lighting conditions which the training data may not capture properly. Also the training data mostly consists of only low-resolution images
Its feature extraction capability does not let small features vanish as we go to deeper layers and
At the same time it can improve speed and accuracy for small dent detection on low resolution images
Change the anchor size – By explicitly providing the network with information about the size of objects by using relevant anchor size helps in improving small object detection
Splitting image into tiles – As we reduce the size of image to fit the network say 448*448 of Yolo many small features will be lost . To avoid this, we split the images to preserve small objects.
Tap the network early – by detecting till where small objects were going, earlier tap points were added by reducing the layers
With Such techniques and customization of FPN etc the accuracy of detection was improved compared to using normal networks. Apart from this many pre/post processing techniques were applied to the data to suit the problem