Xidian University

Rui Lai

Huan Li

Jun Zhou

Bo Yao

Yue Yu

Xiuping Jia

Jiangluqi Song

Dong Zhao

Hanlin Qin

Bingjian Wang

Huixin Zhou

Lixin Guo

Jiajia Zhang

Yuanbin Shi

Pei Xiang

Baokai Deng

A detection and positioning method for a train water injection port includes: acquiring train water injection port video images, and performing threshold segmentation on the train water injection port video images to obtain binarized train water injection port video images; processing the binarized train water injection port video images and matching the processed binarized train water injection port video images with a train water injection port template image; detecting a position of the water injection port in the train water injection port video image and comparing the position with a pre-set position range where the water injection port is located; and if the position and the pre-set position range have been matched, then transmitting a matching valid signal to a mechanical device control module to control a mechanical device to move or stop and start or stop water injection.

Detection and positioning method for train water injection port

A feature fusion and dense connection-based method for infrared plane object detection includes: constructing an infrared image dataset containing an object to be recognized, calibrating a position and class of the object to be recognized in the infrared image dataset, and obtaining an original known label image; dividing the infrared image dataset into a training set and a validation set; performing image enhancement preprocessing on images in the training set, performing feature extraction and feature fusion, and obtaining classification results and bounding boxes through a regression network; calculating a loss function according to the classification results and the bounding boxes in combination with the original known label image, and updating parameter values of a convolutional neural network; repeating the steps to iteratively update the parameters of the convolutional neural network; and processing images in the validation set through the parameters to obtain a final object detection result map.

Feature fusion and dense connection-based method for infrared plane object detection

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Xi'an, China

Rui Lai