A Unified Framework for Automobile Instrument Detection System

As a critical interface between humans and automobiles, the precision of automobile instruments is directly related to safety. During production testing, the check for automobile instruments is essential. Considering that a manufacturing line is necessary to detect various types of instruments frequently, this article proposes a unified and generalized detection framework for pointer meters. The proposed framework innovatively gives the corresponding solutions in view of the existing problems for each subtask. First, a pointer-free template is established by pixelwise background modeling. Then, the two-stage similarity measurement based on fuzzy theory and image reconstruction is designed to enhance images and emphasize pointers, boosting the generalization of the detection framework. For the pointer positioning, the edges of pointers are fit by the set-to-set distances introducing the directionality and intensity of pointers to improve the robustness. For the scale marks & x2019; positioning, the spatial periodicity of scale marks is creatively introduced to address the challenges caused by noises and the connected scale marks. Finally, the indicator value (the value that the pointer points to) is calculated according to the obtained positions of the pointer and scale marks. Experimental results show that the proposed detection framework can reliably detect various types of pointer meters, even under complex illumination conditions.

Automated summary

The proposed framework in this article provides a unified and generalized detection method for pointer meters by using techniques such as background modeling, similarity measurement, and point-to-point distance fitting to enhance images, locate pointers and scale marks, and calculate indicator values.