Hongwei Sun

Aiming at the problem that the embedded platform cannot meet the real-time detection of multisource images, this paper proposes a lightweight target detection network MNYOLO suitable for embedded platforms using deep separable convolution instead of standard convolution to reduce the number of model parameters and calculations; at the same time, the visible light target detection model is used as the pretraining model of the infrared target detection model and the infrared target data set collected on the spot is fine-tuned to obtain the infrared target detection model. On this basis, a decision-level fusion detection model is obtained to realize the complementary information of infrared and visible light multiband information. The experimental results show that it has a more obvious advantage in detection accuracy than the single-band target detection model while the decision-level fusion target detection model meets the real-time requirements and also verifies the effectiveness of the above algorithm.

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.