Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is propose...Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.展开更多
It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors.In this paper,an Oplegnathus image dataset for fish behaviors study by deep learning algorithm i...It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors.In this paper,an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed,and the data is captured from two cameras(one above water and another below water);and then an improved Neural Network model based on multi-scale features is proposed for fish behaviors learning auto-matically.To overcome the occlusion and blur problems of the images,the lightweight neu-ral network MobileNet-SSD is improved by adding a dilate convolution,and SE blocks are added to the feature maps at different scales to establish a self-attention mechanism;the Focal Loss function is used to calculate the classification loss and to balance the propor-tion of background and target samples.The results of the experiments show that the aver-age behaviors detection accuracy of our method reach 90.94%and 88.36%in both overwater and underwater datasets.展开更多
基金the National Natural Science Foundation of China (Grant Nos.51575516 and 51605481)Xi'an Science and Technology Project(Grant No. 2017089CG/RC052 HJKC001).
文摘Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.
基金The work of this paper is jointly supported by the National Natural Science Foundation of China(U1706220,61472172)the Yantai Key R&D Project(2017ZH057,2018ZDCX003,2019XDHZ084).
文摘It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors.In this paper,an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed,and the data is captured from two cameras(one above water and another below water);and then an improved Neural Network model based on multi-scale features is proposed for fish behaviors learning auto-matically.To overcome the occlusion and blur problems of the images,the lightweight neu-ral network MobileNet-SSD is improved by adding a dilate convolution,and SE blocks are added to the feature maps at different scales to establish a self-attention mechanism;the Focal Loss function is used to calculate the classification loss and to balance the propor-tion of background and target samples.The results of the experiments show that the aver-age behaviors detection accuracy of our method reach 90.94%and 88.36%in both overwater and underwater datasets.
