Identification of Different Car Seat Headrests with CNNs
For the identification for headrests in automotive seats for a leading European tier-1 supplier NEA-ROB for Headrest Inspection vision system was developed. The solution is based on computer vision and AI using deep neural networks presenting an accuracy rate of 99.95%.
Besides the analysis of complex objects (textured and geometry 3D complex) more restrictive analysis thresholds were needed. This type of approuch was applied by Neadvance to develop an automatic solution for identification of headrests by generating a unitary code, meeting the requirements of productive traceability postulated by the OEM from the tier 1 supplier. The solution consists of a robotised system that identifies the different backrest models through visual characteristics extracted from the images of these models. 101 distinct models of backrests are distinguished by morphology (eleven distinct volumes), material (type, texture, markings) and seat stitch (type, colour).
Shape and Texture Analysis
The research in shape and texture analysis originated a diverse range of techniques for extracting characteristics of the images both in the spatial domain and in the spectral domain, which when combined with the design of specific classifiers allowed the identification and classification of the same objects. Concerning the classifiers applied, the approaches are diversified, highlighting those based on methods derived from the domain of statistics and AI. With the actual maturation of deep neural networks in particular with CNN’s (convolutional neural networks) and the improvements in GPU technology these methods are now manageable. The different topologies of CNN’s are state of the art in the domain of texture analysis, recognition of patterns and objects. There are several network typologies, e.g. ResNet, DenseNet and Inception. The application described implements an Inception V3 ConvNet.