Research on the performance of the SegFormer model with fusion of edge feature extraction for metal corrosion detection.

Addressing the challenge that existing deep learning models face in accurately segmenting metal corrosion boundaries and small corrosion areas. In this paper, a SegFormer metal corrosion detection method based on parallel extraction of edge features is proposed. Firstly, to solve the boundary ambiguity problem of metal corrosion images, an edge-feature extraction module (EEM) is introduced to construct a spatial branch of the network to assist the model in extracting shallow details and edge information from the images.

Tamm-cavity terahertz detector.

Efficiently fabricating a cavity that can achieve strong interactions between terahertz waves and matter would allow researchers to exploit the intrinsic properties due to the long wavelength in the terahertz waveband. Here we show a terahertz detector embedded in a Tamm cavity with a record Q value of 1017 and a bandwidth of only 469 MHz for direct detection. The Tamm-cavity detector is formed by embedding a substrate with an NbN microbolometer detector between an Si/air distributed Bragg reflector (DBR) and a metal reflector.

ADE-CycleGAN: A Detail Enhanced Image Dehazing CycleGAN Network.

The preservation of image details in the defogging process is still one key challenge in the field of deep learning. The network uses the generation of confrontation loss and cyclic consistency loss to ensure that the generated defog image is similar to the original image, but it cannot retain the details of the image. To this end, we propose a detail enhanced image CycleGAN to retain the detail information during the process of defogging.

AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection.

Small object detection is one of the key challenges in the current computer vision field due to the low amount of information carried and the information loss caused by feature extraction. You Only Look Once v5 (YOLOv5) adopts the Path Aggregation Network to alleviate the problem of information loss, but it cannot restore the information that has been lost. To this end, an auxiliary information-enhanced YOLO is proposed to improve the sensitivity and detection performance of YOLOv5 to small objects.

Development and validation of an APCI-MS/GC-MS approach for the classification and prediction of Cheddar cheese maturity.

Headspace techniques have been extensively employed in food analysis to measure volatile compounds, which play a central role in the perceived quality of food. In this study atmospheric pressure chemical ionisation-mass spectrometry (APCI-MS), coupled with gas chromatography-mass spectrometry (GC-MS), was used to investigate the complex mix of volatile compounds present in Cheddar cheeses of different maturity, processing and recipes to enable characterisation of the cheeses based on their ripening stages. Partial least squares-linear discriminant analysis (PLS-DA) provided a 70% success rate in correct prediction of the age of the cheeses based on their key headspace volatile profiles.