Quadcopters have become a unmanned aerial vehicle class playing essential roles in a wide range of real-world applications. Many missions in such applications require the quadcopter’s attitude to converge to given setpoints in a fixed time. However, existing controllers deliver fixed-time convergence at a high cost of energy consumption. This paper presents a fixed-time quadcopter attitude controller, which advances existing quadcopter control algorithms in terms of convergence speed and energy consumption. First, the quadcopter dynamics is presented. Second, based on the dynamical model, an improved fixed-time sliding mode controller is proposed. Next, the fixed-time convergence and stability of the closed-loop system are strictly analyzed by using the Lyapunov theory. Additionally, numerical simulations are conducted to verify the feasibility and efficacy of the proposed method. Through comparisons with closely related works, the effectiveness of our algorithm is emphasized. Finally, experimental results demonstrate the feasibility and applicability of our work in real-world systems.

Semantic segmentation of drone imagery is critical for applications such as autonomous navigation, environmental monitoring, and aerial surveillance. However, modern deep learning models, such as Vision Transformers (ViTs), despite their success in various vision tasks, often entail high computational costs due to the complexity of their self-attention mechanisms, limiting their deployability on resource-constrained drone systems. This paper proposes a novel lightweight U-Net-based architecture, termed ITE-U-Net, which incorporates an efficient Identity Transformer Encoder to balance high-performance semantic segmentation with reduced computational complexity. By substituting the computationally intensive self-attention mechanism in ViTs with a streamlined yet effective token mixing strategy, ITE-U-Net preserves robust feature extraction capabilities while significantly reducing resource requirements. Additionally, spatial pyramid pooling is integrated at the bottleneck to enhance multi-scale feature extraction, and Swish activation is employed to have smoother gradients and improved convergence. Experimental evaluations conducted on both benchmark and real-world drone-based image data reveal that ITE-U-Net delivers competitive segmentation performance compared to state-of-the-art models, while significantly reducing computational demands. Furthermore, a case study implemented in a controlled environment confirms the practical utility of the proposed framework under realistic conditions, establishing it as an efficient and scalable solution for semantic segmentation within drone vision systems.

Growth factors affect farm owners, environmental conditions, nutrient adaptation, and resistance to chrysanthemum diseases. Healthy chrysanthemum plants can overcome all these factors and provide farms owners with a lot of income. Chrysanthemum white rust disease is a common disease that occurs worldwide; if not treated promptly, the disease spreads to the entire leaf surface, causing the plant’s leaves to burn, turn yellow, and fall prematurely, reducing the photosynthetic performance of the plant and the appearance of the flower branches. In Korea, chrysanthemum white rust disease most often occurs during the spring and autumn seasons, when temperature varies during the summer monsoon, and when ventilation is poor in the winter. Deep neural networks were used to determine healthy and unhealthy plants. We applied the Raspberry Pi 3 module to recognize white rust and test four neural network models. The five main deep neural network processes utilized for a dataset of non-diseased and white rust leaves include: (1) data collection; (2) data partitioning; (3) feature extraction; (4) feature engineering; and (5) prediction modeling based on the train–test loss of 35 epochs within 20 min using Linux. White rust recognition is performed for comparison using four models, namely, DenseNet-121, ResNet-50, VGG-19, and MobileNet v2. The qualitative white rust detection system is achieved using a Raspberry Pi 3 module. All models accomplished an accuracy of over 94%, and MobileNet v2 achieved the highest accuracy, precision, and recall at over 98%. In the precision comparison, DenseNet-121 obtained the second highest recognition accuracy of 97%, whereas ResNet-50 and VGG-19 achieved slightly lower accuracies at 95% and 94%, respectively. Qualitative results were obtained using the Raspberry Pi 3 module to assess the performance of the seven models. All models had accuracies of over 91%, with ResNet-50 obtaining a value of 91%, VGG-19 reaching a value of 93%, DenseNet-121 reaching 95%, SqueezeNet obtaining over 95%, MobileNet obtaining over 96%, and MobileNetv2-YOLOv3 reaching 92%. The highest accuracy rate was 97% (MobileNet v2). MobileNet v2 was validated as the most effective model to recognize white rust in chrysanthemums using the Raspberry Pi 3 system. Raspberry Pi 3 module was considered, in conjunction with the MobileNet v2 model, to be the best application system. MobileNet v2 and Raspberry Pi require a low cost for the recognition of chrysanthemum white rust and the diagnosis of chrysanthemum plant health conditions, reducing the risk of white rust disease and minimizing costs and efforts while improving floral production. Chrysanthemum farmers should consider applying the Raspberry Pi module for detecting white rust, protecting healthy plant growth, and increasing yields with low-cost.

This paper proposes Global Saturation-Value Translation (GSVT), a novel single-image dehazing technique that extends the principles of the Regional Saturation-Value Translation (RSVT) algorithm to achieve comprehensive haze removal across an entire image. While RSVT effectively mitigates haze in specific bright regions by leveraging localized saturation and value shifts in the HSV color space, its regional focus limits its applicability to heterogeneous scenes with varying haze densities. The proposed GSVT framework addresses this limitation by proposing a globally adaptive translation mechanism that dynamically adjusts saturation and value components across all image regions, guided by a spatially-aware translation map. By preserving hue consistency and employing a global coordinate shift in the saturation-value plane, GSVT ensures uniform dehazing without the need for soft segmentation, reducing computational complexity while enhancing color fidelity and detail recovery. Experimental results on synthetic and real-world data demonstrate that GSVT performs competitively when compared with state-of-the-art methods in terms of quantitative metrics and visual quality.

This paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insights derived from the HSV color space: first, the hue component shows negligible variation between corresponding hazy and haze-free points; and second, in the 2D saturation-value coordinate system, the majority of lines connecting hazy-clean point pairs tend to converge near the atmospheric light coordinates. Consequently, haze removal can be achieved through appropriate translations within the saturation-value coordinates. Additionally, a robust soft segmentation method that employs a morphological min-max channel is integrated into the framework. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is established. Experimental evaluations across various datasets demonstrate that the proposed approach effectively mitigates color distortion and successfully restores visually appealing images. Moreover, a case study involving actual flight test demonstrates the feasibility and effectiveness of the proposed approach in real-world scenarios. The code is available at https://github.com/tranleanh/rsvt.

Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-based human detection system is able to improve the ability to save more lives. To this end, a thermal and infrared imaging fusion strategy based on multiple cameras for human detection in low-visibility scenarios caused by smoke is proposed in this paper. By processing with multiple cameras, vital information can be gathered to generate more useful features for human detection. Firstly, the cameras are calibrated using a Light Heating Chessboard. Afterward, the features extracted from the input images are merged prior to being passed through a lightweight deep neural network to perform the human detection task. The experiments conducted on an NVIDIA Jetson Nano computer demonstrated that the proposed method can process with reasonable speed and can achieve favorable performance with a mAP@0.5 of 95%.