Convolutional neural network-based detection models have been extensively applied in industrial production for monitoring the use of safety protection equipment, ensuring worker safety. This paper addresses scenarios in electrical operations where th...
This paper presents a new adaptive downsampling technique called elastic downsampling, which enables high compression rates while preserving the image quality. Adaptive downsampling techniques are based on the idea that image tiles can use different...
In this work, we present a compact “adaptive downsampling” method that mitigates the nonlinearity problems associated with FPGA-based TDCs that utilize delay lines. Additionally, this generic method allows for trade-offs between resolutio...
The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning meth...
In recent years, channel state information (CSI)-based sensing technology has gradually attracted widespread attention as a contactless and low-cost approach for robotic arm motion understanding. Despite continuous progress in CSI-based human sensing...
This paper proposes a method of transmitting video streaming data based on downsampling-upsampling pyramidal decomposition. By implementing an octal tree decomposition of the frame cubes, prior to transforming them into hypercubes, the algorithm mana...
To address the accuracy degradation of ship detection caused by occlusion from adjacent vessels, shore-based facilities and meteorological obscuration in complex maritime-surveillance scenes, this paper proposes an occlusion-robust detection model na...
Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for...
Corn, as a globally significant food crop, faces significant yield reductions due to competitive growth from weeds. Precise detection and efficient control of weeds are critical technical components for ensuring high and stable corn yields. Tradition...
Fire detection in warehouse scenarios is challenged by complex background interference, weak features of small flame targets, and indistinct flame textures, which can reduce detection performance and constrain model deployment. To address these issue...
Detecting unmanned aerial vehicles (UAVs) in complex low-altitude scenes remains challenging due to limited target details and background interference. To address these challenges, this paper proposes DARA-Net, a lightweight UAV detection framework t...
Neural network architecture search (NAS) technology is pivotal for designing lightweight convolutional neural networks (CNNs), facilitating the automatic search for network structures without requiring extensive prior knowledge. However, NAS is resou...
Detecting small and distant objects in road traffic scenarios remains challenging owing to limited pixel resolution, cluttered backgrounds, and resource constraints on edge computing platforms. This work presents LCA-Net, a computationally efficient...
UAV object detection is still difficult to achieve due to large-scale variation, dense small objects, a complicated background, and resource constraints from onboard computing. To solve these problems, we develop a diffusion-enhanced detection networ...
Infrared (IR) video target detection is important for long-range target perception in anti-UAV systems under complex lighting and background conditions. However, long-range tiny unmanned aerial vehicles (UAVs) usually occupy only a few pixels, exhibi...
In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved v...
Plant species, structural combination, and spatial distribution in different regions should be adapted to local conditions, and the reasonable arrangement can bring the best ecological effect. Therefore, it is essential to understand the classificati...
The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy...
To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion...
This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual...
Small object detection (SOD) in optical remote sensing images (RSI) is essential for aerial perception yet remains challenged by the severe degradation of fine-grained spatial features during downsampling and the high sensitivity of Intersection over...
Recent advancements in intelligent transportation systems have enabled smart vehicles to autonomously detect, predict, and respond to potential hazards in real time. However, achieving sub-second reaction performance remains challenging due to comput...
In remote sensing imagery, detecting small objects is challenging due to the limited representational ability of feature maps when resolution changes. This issue is mainly reflected in two aspects: (1) upsampling causes feature shifts, making feature...
In remote sensing image scene classification (RSISC) tasks, downsampling is crucial for reducing computational complexity and cache demands, enhancing the model’s generalization capability of deep neural networks. Traditional methods, such as r...
Accurate and efficient detection of surface damage on wind turbine blades is important for ensuring the safe operation and maintenance of wind farms. Existing models have problems of irreversible loss of micro-damage features, insufficient modeling o...
Point cloud processing of raw scan data is a critical step to enhance the accuracy and efficiency in computer-aided inspection and remanufacturing of damaged aero-engine blades. This paper presents a new methodology to obtain a noise-reduced and simp...
The sustainable cultivation of agarwood, a high-value tree species, is significantly threatened by foliar pests, requiring efficient and accurate monitoring solutions. While deep learning is widely used, mainstream models face inherent limitations: C...
In recent years, deep convolutional neural networks (DCNNs) have made strong progress on single-image super-resolution (SISR). Most of them work in a fully supervised way: they learn a non-linear mapping from a low-resolution (LR) image to its high-r...
To address the sampling misalignment and detail loss caused by fixed-grid downsampling for small-scale defects, as well as the insufficient differentiated modeling and interaction of defect details and structural context in UAV-acquired power-line im...
This study proposes a feature-enhanced diffusion model based on wavelet transform and Mamba to address the issues of low audio realism, inadequate text relevance, and slow inference speed in text-guided sound effect generation. A wavelet transform-ba...
In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain...
Surface defect evaluation in steel production demands both high inference speed and accuracy for efficient production. However, existing methods face two critical challenges: (1) the diverse dimensions and irregular morphologies of surface defects re...
The timely and accurate detection of unidentified drones is vital for public safety. However, the unique characteristics of drones in complex environments and the varied postures they may adopt during approach present significant challenges. Addition...
Small-object detection in remote sensing imagery faces two specific challenges that existing lightweight detectors fail to address jointly: the irreversible loss of high-frequency boundary cues during repeated downsampling, and feature smearing betwe...
Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fu...
This paper proposes a novel fault diagnosis method that integrates a Relative Position Matrix (RPM), a Downsampling Attention Module (DAM), an Improved Residual Network (IResNet), and transfer learning to address the challenges of scarce fault data a...
Dehazing of images is important for proper interpretation of optical images in remote sensing. However, current dehazing networks tend to have limited receptive field and texture information loss caused by conventional downsampling and complementary...
A method called iTEKA, which stands for iterative time elastic kernel averaging, was successfully used for averaging time series. In this paper, we adapt it to GPS trajectories. The key contribution is a denoising procedure that includes an over-samp...
We present a skeleton-based approach to baseball pitch type classification from broadcast video. We leverage Human Pose Estimation and an ST-GCN architecture, improved with a projection-gated temporal downsampler, to learn kinematic signatures of the...
Printed Circuit Boards (PCBs) are essential components in electronic devices, making defect detection crucial. PCB surface defects are diverse, complex, low in feature resolution, and often resemble the background, leading to detection challenges. Th...
Accurate Remaining Useful Life (RUL) prediction of lithium batteries is crucial for enhancing their performance and extending their lifespan. Existing studies focus on continuous or relatively sparse datasets; however, continuous and complete dataset...
To improve the performance of vehicle target detection in complex traffic environments and solve the problem that it is difficult to make a lightweight detection model, this paper proposes a lightweight vehicle detection model based on enhanced You O...
Existing drone image processing algorithms for small target detection in Unmanned Aerial Vehicle (UAV) aerial images struggle with challenges like missed detection of small objects, information loss from downsampling, loss of low-dimensional features...
To address the challenges of rapid and accurate grape variety identification in natural orchard environments, along with the demand for efficient deployment on mobile devices, we propose in this paper YOLOv11n-GrapeLite, a lightweight model built upo...
To meet the requirements of accurate multi-class animal detection and model lightweighting in UAV-based grazing monitoring, this study presents DMN-YOLO, an efficient detector built upon YOLO11n. In particular, a lightweight downsampling module, DSDo...
Current management strategies for sorghum aphids heavily rely on indiscriminate chemical application, leading to severe environmental consequences and impacting food safety. While precision spraying offers a viable remediation for pesticide overuse,...
In response to problems such as large target scale variations, strong background noise, and blurred features leading by low contrast in infrared target detection in near space environments, this paper proposes an efficient detection model, YOLO-MARS,...
To address the advancements in jamming technology, it is imperative to consider robust adaptive beamforming (RBF) methods with finite snapshots and gain/phase (G/P) errors. This paper introduces an end-to-end RBF approach that utilizes a two-stage co...
Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UN...
While recent advancements in large-scale pretraining have significantly improved dense geometric models, these architectures often struggle to adapt to 4K-resolution inputs because of their rigidity. Existing benchmarks mostly provide low-resolution...
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