The invention provides an automobile cabin end side model multi-
modal perception enhancement method, which comprises the following steps: S1) deploying an RGB camera in a cabin, and collecting facial biological characteristic data, hand interaction behavior data and cabin overall environment data of a driver; s2) adjusting image illumination and contrast by adopting an
algorithm, dynamically focusing a key area through a target detection frame to process a shielding problem, and eliminating static redundant information; s3) constructing a neural network, extracting facial biological features and the like in parallel, and outputting low-dimensional feature vectors; s4)
time sequence association is established, key area feature weights are enhanced through a space attention mechanism, and a logic relationship among different features is explicitly modeled; s5) aggregating the weighted feature maps by using feature attention
pooling, and retaining a core feature channel in combination with a channel
pruning technology to realize
model compression; s6) dynamically adjusting the calculation priority and
weight distribution of each
feature extraction branch; and S7) the end side uploads the sample to the cloud side, and the cloud side generates an update
package and pushes the update
package to the end side to complete model iteration.