The invention relates to the technical field of
image fusion, and discloses a lightweight and efficient multi-
modal data fusion method. According to the method, an improved MobileNetV3-Small network is adopted as a basic feature extractor, parallel
branch design in a training stage is optimized through a structure re-parameterization technology, and the parallel
branch design is combined into single
convolution in a reasoning stage, so that the calculation cost is remarkably reduced; resNet50 is introduced as a teacher model, a soft
label is generated through temperature scaling, a student model is guided in combination with KL
divergence loss and cosine feature alignment loss, and high-precision
feature extraction of a lightweight network is realized; a dynamic weight
fusion mechanism is designed, microscopic
fluorescence and microfluidic
fluorescence image features are fused, and multi-
modal information contribution is balanced; aiming at the characteristics of different
modal images, preprocessing methods such as guided filtering, CLAHE
contrast enhancement,
time sequence difference artifact removal and the like are respectively adopted, and effective signals are enhanced. According to the
algorithm, the
pathogen detection precision is ensured, the
model parameter quantity is reduced, and an efficient solution capable of being deployed in an embedded mode is provided for
livestock and poultry
epidemic disease screening.