The invention belongs to but is not limited to the technical field of PCBA defect detection, and discloses a PCBA AI defect detection item
demand response method and
system, which adopts multi-scale long-range fusion, context information, super-resolution and
block detection to improve a target detection
algorithm, and performs detection and identification of a tiny target. Identifying the fine
granularity of the target by adopting a
contextual information and depth
feature matching method; 3D
point cloud and X-
ray perspective are adopted, a multi-
sensor fusion detection frame is used, and invisible defect detection is carried out. According to the method, AI technologies such as
deep learning are utilized, the detection precision of small defects on the PCBA board is effectively improved, and missing detection and
false detection are reduced. The AI technology can automatically identify and learn defect features, and efficiency and accuracy are significantly improved. The AI model can optimize the self
algorithm through continuous learning, so that the detection
system can adapt to different PCBA boards and production environments, and the universality and flexibility of the detection
system are enhanced.