LCD Defect Detection Based on Deep Learning and MapReduce

With the rapid development of the optoelectronic communication industry, the requirements for accuracy and efficiency in LCD defect detection are increasing. This paper takes LCD defect detection as the research object, analyzes the principles and performance advantages and disadvantages of three types of algorithms, and introduces the MapReduce distributed computing framework to explore its optimization ideas in LCD defect detection. Comparative analysis reveals that single-stage models offer strong real-time performance but have limited ability to detect subtle defects; two-stage models achieve higher accuracy but suffer from high computational complexity and slow inference speed; combining MapReduce with traditional models can effectively improve the processing efficiency of large-scale detection tasks. Research indicates that the combination of distributed computing and deep learning-based object detection can significantly improve efficiency while maintaining detection accuracy, providing a feasible technical path for improving the speed and accuracy of LCD defect detection.