Lens module appearance detection method and system based on SSD algorithm

This patented technology allows for better understanding about how well different types of optical devices work together when used at close range without being damaged or affect their performance significantly over time. By combining specific mathematical techniques like convolutional neural networks and Focal Layer Convulsive Potential Functions (FLC), we aim to enhance the functionality of Optical Devices that detect Small Target Objects (SSO). These improvements include optimizing the number of filters needed based upon the device's resolution capabilities, increasing sensitivity towards smaller targets, reducing noise interference between images captured simultaneously during imagery capture, and adding support functions such as rasterization processing to optimize the output data volume while maintaining high-quality video quality. Overall, our technical effects help us create advanced technologies for protecting sensitive components within electronic equipment against external influences caused by impact force.

This patents describes how faulty images caused by external forces like impact may affect the quality of photos taken by cameras installed inside vehicles. Current solutions involve manual inspections where operators inspect each frame individually before sending new ones, leading to slowdowns and increased workload. There also exist automatic systems that use machine intelligence (ML) techniques to automatically analyze frames captured by camers without requiring trained experts. These technical problem addressed by this patented technology include improving automated defect analysis tools for lensed module damage identification, reducing labor costs associated with manual testing, enabling accurate damage assessment across multiple scenes, increasing efficiency over existing approaches involving humans, and providing effective ways to quickly respond to detected threats.