Search

• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. To address the issue of information loss, we introduce multi-threshold spiking neurons, which improve the efficiency of information transmission within the Spiking-UNet. For the training strategy, we adopt a conversion and fine-tuning pipeline that leverage pre-trained U-Net models. During the conversion process, significant variability in data distribution across different parts is observed when utilizing skip connections. Therefore, we propose a connection-wise normalization method to prevent inaccurate firing rates. Furthermore, we adopt a flow-based training method to fine-tune the converted models, reducing time steps while preserving performance. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart, surpassing existing SNN methods. Compared with the converted Spiking-UNet without fine-tuning, our Spiking-UNet reduces inference time by approximately 90%. This research broadens the application scope of SNNs in image processing and is expected to inspire further exploration in the field of neuromorphic engineering. The code for our Spiking-UNet implementation is available at https://github.com/SNNresearch/Spiking-UNet. [ABSTRACT FROM AUTHOR]

Despite massive development in aerial robotics, precise and autonomous landing in various conditions is still challenging. This process is affected by many factors, such as terrain shape, weather conditions, and the presence of obstacles. This paper describes a deep learning-accelerated image processing pipeline for accurate detection and relative pose estimation of the UAV with respect to the landing pad. Moreover, the system provides increased safety and robustness by implementing human presence detection and error estimation for both landing target detection and pose computation. Human presence and landing pad location are performed by estimating the presence probability via segmentation. This is followed by the landing pad keypoints' location regression algorithm, which, in addition to coordinates, provides the uncertainty of presence for each defined landing pad landmark. To perform the aforementioned tasks, a set of lightweight neural network models was selected and evaluated. The resulting measurements of the system's performance and accuracy are presented for each component individually and for the whole processing pipeline. The measurements are performed using onboard embedded UAV hardware and confirm that the method can provide accurate, low-latency feedback information for safe landing support. • An approach to vision-based detection of landing target for autonomous UAV missions. • Near real-time performance of pipeline is confirmed on modern embedded GPU devices. • Increased safety and robustness by human presence detection and error estimation. • Pipeline's accuracy as measured using the OptiTrack system confirms its capability. [ABSTRACT FROM AUTHOR]

• Frequency-domain feature enhancement and a spatial attention mechanism are employed to enhance the accuracy of misalignment sensing. • The proposed Fourier-attention neural network achieves a two-dimensional misalignment sensing accuracy of 0.23 nm at a frequency of 20 Hz. • The experimental results of the proposed strategy illustrate its robustness to both system errors and noise. A recurring challenge in integrated lithography is subnanoscale misalignment sensing. In widely-used moiré-based misalignment sensing schemes, measurement accuracy is restricted by the performance of the image processing schemes. This is also a fundamental problem in the field of Fourier optics that has received extensive attention in the science and engineering fields. This paper proposes a Fourier-attention neural network that can achieve real time-lapse misalignment sensing with an accuracy of 0.23 nm. This is enabled by the system's robustness to system errors and noise. We hope that this strategy can provide an effective solution for various misalignment sensing applications and that the approach can be applied to future problems. [ABSTRACT FROM AUTHOR]

The metasurface-based splitters have demonstrated great potential in high-resolution, dark-scene, and ultra-fast imaging due to their excellent capabilities of preserving light intensity, attracting increasingly more research interests. This paper investigates the splitter pattern design, which determines the theoretical performance limit of metasurface-based color imaging systems, to guide their further development. We first establish a unified mathematical model for the splitter-based imaging process. This model reveals that the raw image detected by the splitters is degraded by noise, incomplete sampling, and blurring. Based on this, we develop a deep neural network capable of simultaneously tackling multiple image processing tasks to achieve color image reconstruction. We then conduct evaluation experiments for several existing patterns, through which we establish three design principles to guide the optimization of a splitter pattern. Furthermore, by jointly learning the parameters of our proposed imaging model and reconstruction network, we achieve an automatic optimization of a splitter pattern. The experiments demonstrate that the optimized pattern can achieve better quantitative and visual results than the original pattern, validating the effectiveness of this pattern optimization method. • Establish a unified mathematical model for metasurface-based color imaging systems. • Identify three principles for designing metasureface-based color splitter patterns. • Develop a method for jointly optimizing splitter pattern and image reconstruction. [ABSTRACT FROM AUTHOR]