Unmanned aerial vehicles (UAVs) are critical in the automated inspection of wind turbine blades. Nevertheless, several issues persist in this domain. Firstly, existing inspection platforms encounter challenges in meeting the demands of automated inspection tasks and scenarios. Moreover, current blade stop angle estimation methods are vulnerable to environmental factors, restricting their robustness. Additionally, there is an absence of real-time blade detail prioritized exposure adjustment during capture, where lost details cannot be restored through post-optimization. To address these challenges, we introduce a platform and two approaches. Initially, a UAV inspection platform is presented to meet the automated inspection requirements. Subsequently, a Fermat point based blade stop angle estimation approach is introduced, achieving higher precision and success rates. Finally, we propose a blade detail prioritized exposure adjustment approach to ensure appropriate brightness and preserve details during image capture. Extensive tests, comprising over 120 flights across 10 wind turbine models in 5 operational wind farms, validate the effectiveness of the proposed approaches in enhancing inspection autonomy.

Dynamic Vision Sensor (DVS)-based solutions have recently garnered significant interest across various computer vision tasks, offering notable benefits in terms of dynamic range, temporal resolution, and inference speed. However, as a relatively nascent vision sensor compared to Active Pixel Sensor (APS) devices such as RGB cameras, DVS suffers from a dearth of ample labeled datasets. Prior efforts to convert APS data into events often grapple with issues such as a considerable domain shift from real events, the absence of quantified validation, and layering problems within the time axis. In this paper, we present a novel method for video-to-events stream conversion from multiple perspectives, considering the specific characteristics of DVS. A series of carefully designed losses helps enhance the quality of generated event voxels significantly. We also propose a novel local dynamic-aware timestamp inference strategy to accurately recover event timestamps from event voxels in a continuous fashion and eliminate the temporal layering problem. Results from rigorous validation through quantified metrics at all stages of the pipeline establish our method unquestionably as the current state-of-the-art (SOTA). The code can be found at bit.ly/v2ce.

Unmanned Aerial Vehicles (UAVs) play a crucial role in meteorological research, particularly in environmental wind field measurements. However, several challenges exist in current wind measurement methods using UAVs that need to be addressed. Firstly, the accuracy of measurement is low, and the measurement range is limited. Secondly, the algorithms employed lack robustness and adaptability across different UAV platforms. Thirdly, there are limited approaches available for wind estimation during dynamic flight. Finally, while horizontal plane measurements are feasible, vertical direction estimation is often missing. To tackle these challenges, we present and implement a comprehensive wind estimation algorithm. Our algorithm offers several key features, including the capability to estimate the 3-D wind vector, enabling wind estimation even during dynamic flight of the UAV. Furthermore, our algorithm exhibits adaptability across various UAV platforms. Experimental results in the wind tunnel validate the effectiveness of our algorithm, showcasing improvements such as wind speed accuracy of 0.11 m/s and wind direction errors of less than 2.8°. Additionally, our approach extends the measurement range to 10 m/s.Wind Estimation, Disturbance Observer, Aerial Robotics, Field Robots

Symptom status tracking poses a significant challenge due to the intricate nature of symptom identification and inference from medical doctor-patient dialogues. Numerous prior studies in this domain have relied on approaches involving multi-label classification and multi-task learning. Multi-label classification methods typically consider symptoms and statuses within a unified label space. Nevertheless, this approach frequently results in sparse predictions, eroding semantic relationships among labels and causing instability in prediction outcomes. In contrast, multi-task learning segregates symptom prediction and status prediction into separate tasks, thereby improving performance relative to conventional multi-label classification methods. Nonetheless, despite these advancements, the imbalance in training task weights persists, leading to suboptimal performance. To tackle these challenges, we employ a cascaded model structure rooted in the Question-Answering (QA) paradigm in this study. Our approach utilizes dialogue content to create context-inquiry pairs and introduces two novel modules: inquiry-aware attention and multi-perception pooling. Inquiry-aware attention enhances the contextual relationship between inquiries and dialogues, while multi-perception pooling extracts diverse semantics from the dialogue. The experimental results unequivocally demonstrate our method's efficiency, surpassing state-of-the-art techniques in symptom status tracking and indicating its superior effectiveness.

Technology-mediated dance experiences, as a medium of entertainment, are a key element in both traditional and virtual reality-based gaming platforms. These platforms predominantly depend on unobtrusive and continuous human pose estimation as a means of capturing input. Current solutions primarily employ RGB or RGB-Depth cameras for dance gaming applications; however, the former is hindered by low-light conditions due to motion blur and reduced sensitivity, while the latter exhibits excessive power consumption, diminished frame rates, and restricted operational distance. Boasting ultra-low latency, energy efficiency, and a wide dynamic range, neuromorphic cameras present a viable solution to surmount these limitations. Here, we introduce YeLan, a neuromorphic camera-driven, three-dimensional, high-frequency human pose estimation (HPE) system capable of withstanding low-light environments and dynamic backgrounds. We have compiled the first-ever neuromorphic camera dance HPE dataset and devised a fully adaptable motion-to-event, physics-conscious simulator. YeLan surpasses baseline models under strenuous conditions and exhibits resilience against varying clothing types, background motion, viewing angles, occlusions, and lighting fluctuations.

Abstract Convolutional- and Transformer-based backbone architecture are two dominant, widely accepted, models in computer vision. Nevertheless, it is still a challenge, thus a focus of research, to decide which backbone architecture performs better, and under which circumstances. In this paper, we conduct an in-depth investigation into the differences of the macroscopic backbone design of the CNN and Transformer models with the ultimate purpose of developing new models to combine the strengths of both types of architectures for effective image classification. Specifically, we first analyze the model structures of both models and identified four main differences, then we design four sets of ablation experiments using the ImageNet-1K dataset with an image classification problem as an example to study the impacts of these four differences on model performance. Based on the experimental results, we derive four observations as rules of thumb for designing a vision model backbone architecture. Informed by the experiment findings, we then conceive a novel model called CMNet which marries the experiment-proved best design practices of CNN and Transformer architectures. Finally, we carry out extensive experiments on CMNet using the same dataset against baseline classifiers. Initial results prove CMNet achieves the highest top-1 accuracy of 80.08% on the ImageNet-1K validation set, this is a very competitive value compared to previous classical models with similar computational complexity. Details of the implementation, algorithms and codes, are publicly available on Github: https://github.com/Arwin-Yu/CMNet .