The invention discloses an intelligent
video image retrieval method based on a neural network self-temperature fault and a knowledge conduction mechanism, which improves the retrieval precision of a small model while ensuring the real-time performance of the model, and achieves balance between the precision and the efficiency as far as possible. A
gamma correction module is arranged, and through local adjustment of the image, illumination non-uniformity robustness is achieved, detail discernibility is improved, high-
frequency noise is avoided, and universality is high; a self-temperature fault mechanism is established, local self-supervision, continuous reflection and learning parameter adjustment of the neural network are allowed, deep
semantic information of the image is fully learned,
rapid convergence of the neural network is achieved, and retrieval precision is improved; a knowledge conduction mechanism is adopted, the model precision is improved, the model time
delay is reduced, network parameters are compressed, and finally a student model with high performance and high precision is obtained; and taking shallow feature knowledge as a learning target through a conduction mechanism, reconstructing deep features by adopting a VAE variational self-encoding model so as to generate a learning result, and measuring the learning result and the target so as to complete a learning task.