RESUMO
Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields.
RESUMO
A high-quality cadmium manganese tellurium (Cd0.9Mn0.1Te: V or VCMT) crystal was successfully grown via modified Te solution vertical Bridgman method with vanadium doping. The crystal structure and quality were evaluated by powder X-ray diffraction analysis. An infrared transmission spectroscope measured the transmittance of the crystal at 64%, which would suggest that the grown crystal possessed high purity and crystallinity. Ultraviolet-visible-near-infrared spectroscopy analysis obtained the forbidden band width of approximately 1.577 eV. The current-voltage test indicated that the VCMT crystal had a high resistivity of 2.07 × 1010 Ω·cm. Mechanical properties were measured by a Vickers microhardness tester. Crack surface morphology around the indentation was recorded. Furthermore, mechanical properties, such as microhardness, fracture toughness, brittleness index and yield strength were investigated and discussed. The thermal stability of the VCMT single crystal was determined by thermogravimetric analysis. A VCMT detector was fabricated with planar configuration structure, which showed a resolution of 11.62% of the 241Am at 59.5 keV peak.