Infrared Physics & Technology最新文献

The laser speckle contrast imaging technique based on the dynamic light scattering theory presents a non-scanning and wide-field method for blood flow imaging. However, its accuracy in biological tissues is limited to the decreased contrast and reduced image clarity as conventional single-exposure approaches are susceptible to static scattering. In this paper, based on the adaptive window space direction contrast (awsdK) imaging method proposed by the laboratory, combined with the optimized single exposure technology, the effect of static scattering under a single exposure is reduced. The experimental results show that the method can effectively correct static scattering and eliminate the effect of system noise on speckle contrast. This method not only improves the imaging quality, but also realizes the rapid monitoring of blood flow changes by using the speckle contrast ratio measured in a single exposure, which provides an effective solution for the further development of laser speckle contrast imaging technology.

Infrared-based road scene object detection algorithms often face issues with excessive parameters and computational demands, making them incompatible with edge devices having constrained computational capabilities. This paper introduces an enhanced lightweight infrared-based road object detection algorithm based on YOLOv8n. Firstly, a streamlined network architecture is devised by merging YOLOv8n’s C2f module with PConv, creating a lighter module and reducing the neural network’s downsampling rate of infrared images. This strategy reduces redundant computations and memory access, preventing the loss of fine details in infrared images caused by deep convolutional neural networks. Additionally, the model’s accuracy in detecting infrared targets is significantly enhanced through the integration of the coordinate attention mechanism. Finally, replacing CIoU with Wise-IoU for bounding box regression in YOLOv8n accelerates the model’s convergence. Empirical findings indicate that in contrast to the YOLOv8n algorithm, the optimized model showcases a 34.17 % reduction in model size, a 40.35 % decrease in parameters, and a 4.8 % increase in average detection accuracy. This enhanced algorithm not only achieves a lightweight profile but also delivers superior performance on embedded edge devices.

Laser beam welding (LBW) uses a concentrated laser beam to fuse materials, providing benefits such as a smaller heat-affected zone (HAZ), speed, accuracy, adaptability across multiple materials, and seamless adoption into automation processes. Achieving optimal weld quality with LBW remains difficult due to the need to select the appropriate welding process variables and proper interlayer between dissimilar materials. Due to the high cost and safety risks to operators and equipment in LBW, it is challenging to use the practical experiment. Additionally, welding materials with high thermal conductivity and lower melting temperatures, such as AZ61A and AA7075, is a considerable challenge. Previous research focused on process parameters in laser beam welding of various metals, but not enough consideration has been given to the effect of a titanium interlayer on AA7075 and AZ61A using virtual data. This study aims to fill these gaps by using simulation data to assess the effects of process parameters and the titanium interlayer during LBW of dissimilar materials. A backpropagation neural network with the gradient descent learning rule was used for optimization, and a central composite design was used to predict the optimal process parameter interaction. The result indicates the optimum equivalent strain is obtained at the values of beam radius between 4.5 to 5 mm. The maximum equivalent stress reached during welding speed is between 4 to 4.5 mm/s. The maximum residual stress was obtained at the number of segments of 160. The predicted maximum and average anticipated errors for peak temperature are 0.1655 % and 0.0210 %, while for residual stress it is computed as 0.1766 % and 0.0754 %. The Ti-interlayer in magnesium and aluminum laser welding reduces peak temperatures, allows for uniform energy distribution, minimizes localized heating, and enhances weld quality while lowering residual stress.

Highly Ho³⁺/Tm³⁺ co-doped Bi₂O₃-B₂O₃-Al₂O₃-BaF₂ glasses were fabricated using the melt-quenching technique. The emission, radiation, and gain characteristics of the glass samples at the wavelength of 2.0 μm were examined analyzed. An intense and broad 2.0 µm emission, resulting from the ³F₄→³H₆ transition of Tm³⁺ and the ⁵I₆→⁵I₇ transition of Tm³⁺, was detected under the stimulation of 808 nm LD. The gain coefficient of Ho³⁺ in Ho³⁺/Tm³⁺ co-doped glass reaches 7.81 cm⁻¹(at 2050 nm), which is 7.5 times as high as that of tellurite glass. Meanwhile, the large σ_emi × Δλ_eff(39.84 × 10^-26 cm³) and σ_emi × τ_rad(16.10 × 10^-21 cm² ms) indicated BBTH4 glass possessed a large gain property and broadband. The energy transfer process of Tm³⁺: ³F₄ ↔ Ho³⁺:⁵I₇ was analyzed, the C_D-A is 90 times larger than the C_A-D. The Ho³⁺/Tm³⁺ co-doped Bi₂O₃-B₂O₃-Al₂O₃-BaF₂ glasses show potential as broadband mid-infrared emission gain material.

The objective of infrared and visible image fusion is to generate a unified image that highlights prominent targets and retains intricate texture details, even in scenarios with imbalanced source image information. However, current image fusion algorithms primarily consider factors like illumination, restricting their applicability to certain scenes and compromising their adaptability. To tackle the issue, this paper proposes the DGFusion, which utilizes TWSSLoss to balance the contribution of source images in the fused output, effectively mitigating the limitations linked to relying solely on illumination guidance. Additionally, modality-complement feature attention harmonizer (MCFAH) facilitates cross-modal channel attention learning. This process assigns weights to features and accomplishes fusion by exchanging cross-modal differential information, thereby enriching each feature with details from the other modality. Furthermore, the multi convolution attentive net (MCAN) dynamically adjusts the contributions of features from different modalities. It prioritizes the most expressive characteristics to accentuate complementary information, enabling efficient fusion. In conclusion, our method outperforms seven state-of-the-art alternatives in terms of preserving target details and retaining texture information. Rigorous generalization experiments across five diverse datasets demonstrate the robustness of our DGFusion model in various scenarios.

Infrared (IR) small target detection exerts a significant role in IR early warning and UAV surveillance. However, in the low-altitude slow-speed small (LSS) target detection scene, the existing algorithms cannot effectively suppress high-contrast corners and sparse edges in the low-altitude background, resulting in many false alarms. To solve this problem, we propose an IR LSS target detection method based on fusion of target sparsity and motion saliency (TSMS). In the low-rank sparse model, we introduce a robust dual-window gradient operator to construct a fine local prior, which avoids the influence of highlighted edges and corners; The Geman norm is used to approximate the background rank to accurately estimate the background and effectively extract sparse targets. Then, a motion saliency model based on inter-frame local matching is constructed to accurately extract the inter-frame features of small target. Finally, the real LSS target is obtained by fusing target sparsity and motion saliency. Experiments indicate that, compared with existing advanced methods, the proposed method has stronger robustness and can effectively detect LSS targets under complex low-altitude background.

The convolutional neural networks (CNNs) have shown high success in supervised analysis of hyperspectral images. But, the use of a supervised CNN is not possible for an unsupervised task such as hyperspectral anomaly detection. So, an unsupervised CNN with pre-determined convolutional kernels without requirement to labeled samples or training process is proposed in this work. The proposed method uses the collaborative representation (CR) for background estimate and introduces the global preserving projection (GPP) for dimensionality reduction of it. Then, the convolutional kernels are randomly selected from the reduced CR data. Moreover, two distances in inner and guard windows are defined, which difference of them results in the anomaly score. The CR based unsupervised CNN (CUCNN) method achieves high detection accuracy compared to its counterparts and is more than 9 times faster than other presented unsupervised CNN detectors.

A hundred-watt, linearly-polarized Ho:YAG oscillator, pumped by a 200 W, home-made and s-polarized Tm:YLF laser, was demonstrated. By utilizing a combination of a half-wave plate (HWP) and a thin-film polarizer (TFP), the Tm:YLF laser was split into two orthogonally polarized parts. Each part was significantly reshaped by a system of orthogonally arranged cylindrical lenses and subsequently used to dual-end pump the oscillator. With a total incident pump power of 183 W, a continuous wave (CW) output power of 101 W at 2.1 μm was achieved, with corresponding slope efficiency and optical-to-optical conversion efficiency (OOCE) of 61.6 % and 55.2 %, respectively. Additionally, the beam quality factors, measured in the x and y directions, were 1.59 and 1.35, respectively. Finally, under Q-switched operation at a repetition rate of 20 kHz, a maximum output power of 97 W was achieved, with a corresponding pulse width of less than 34 ns. The central wavelength of the laser was measured to be 2090.2 nm.

We report on the generation of a supercontinuum (SC) by nonlinear polarization-rotating (NPR) mode-locked fiber laser, which includes a pulsed switchable NPR mode-locked fiber laser, an Er-doped fiber amplifier, and high nonlinear fiber (HNLF). In the experiment, by adjusting the polarization controller (PC) angle and pump power, we obtained three different pulses. They are traditional soliton bunch pulses, the coexistence of soliton bunch pulses and square pulses, and multi-longitudinal mode noise like square pulses. All three pulses can generate SC with spectrum width be about of 1000 nm. We fixed the pump power in the laser and observed the influence of amplifier power on the spectrum width and output power of SC. The SC width of soliton bunch pulse reaches 950.9 nm, the SC of soliton bunch pulse and square pulse coexisting pulse is 1009.7 nm, and the SC spectrum width of multi-longitudinal mode noise like square pulse is 887.1 nm. Our results demonstrate the generation of SC with three types of switchable pulses, opening up possibilities for various application requirements.

Topological insulators, as a class of materials with narrow bulk bandgap and gapless surface states with response wavelengths covering infrared to terahertz, have great potential for application in new generation photodetector, but the large dark current and small photocurrent limit their application, so the device performance is generally improved by the method of heterogeneous integration. SnTe, as a topological crystalline insulator with multiple surface states, has a narrower forbidden bandwidth, it is suitable for the fabrication of infrared photodetector. In this work, SnTe thin films were deposited on Si substrates by magnetron sputtering, and SnTe/n-Si heterostructure photodetectors were fabricated on this basis. The photodetector exhibited good photoresponses in the visible near-infrared (532–1400 nm), with the responsivity (R) and normalized detectivity (D*) reaching 1.12 A/W, 5.17 × 10¹¹ Jones. Thanks to the formation of the built-in electric field at the SnTe/Si interface, the photogenerated carriers can be rapidly separated and transported, and the switching ratio reaches 10³. In addition, the rise time and fall time of the device are 218 μs and 174 μs, respectively. The good performance and simple preparation method make the device have a wide application prospect in the new generation of photodetector.