The invention relates to the technical field of
machine vision and electronic manufacturing, in particular to an SMT dispensing
electronic component identification method and
system based on lightweight multiple attention, and the method comprises the steps: firstly designing a cross
shuffling attention (CSA) module, fusing the MobileNetv3 deep
convolution and an improved Si-ShuffleNetv2 structure, and achieving the efficient lightweight extraction of input features; secondly, a ghost multi-attention (GMA) module is constructed, feature dimensions are compressed through GhostConv, a C3CBAM attention mechanism is integrated, and key information
perception in the
feature fusion process is enhanced; and then, in combination with improved robust Canny
edge detection and
Hough transform, accurately extracting an element contour in a complex
industrial noise environment. And finally, multi-coordinate
system conversion and motion track planning are realized through a
robot operating system (ROS). According to the method, dependence of a traditional
template matching method on background styles and artificial parameters is broken through, 17FPS real-time detection is achieved on Jetson Nano edge equipment through a lightweight attention mechanism and model structure optimization, and the effect is outstanding.