Crack detection in cement infrastructure is imperative to ensure its structural integrity and public safety. However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is...
Falls are defined by the World Health Organization (WHO) as incidents in which an individual unintentionally falls to the ground or a lower level. Falls represent a serious public health issue, ranking as the second leading cause of death from uninte...
This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapp...
Visible–thermal object detection benefits from the complementary properties of RGB and thermal imagery, but repeated cross-modal fusion can increase model complexity, particularly in lightweight detectors. This paper proposes SDR-YOLO, a scale-...
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...
All-weather, high-precision perception of airport airside targets such as distant personnel, ground vehicles, and small hazards is essential for safety control. Visible-light monitoring degrades under low illumination, dust, and fog, whereas infrared...
This paper proposes DyMC-YOLO, an efficient RGB–infrared object detector based on YOLOv13. The evaluated infrared modalities include thermal infrared in the M3FD benchmark and near-infrared in a self-constructed RGB–NIR grape dataset. To...
Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance resea...
In low-light, occluded, and cluttered environments, single-modality RGB detectors are prone to false positives and missed detections. While infrared (IR) imaging provides relatively stable target visibility under poor illumination, it lacks texture a...
YOLO-GRAT integrates P3 Cross-Modal Gated Residual Fusion with adaptively weighted Triplet Attention inside backbone C3 bottlenecks of a YOLOv5-based visible–infrared detector. The fusion block combines input-dependent modality mixing, a mean-r...
In indoor environments, the elderly, children, and people with disabilities are prone to abnormal behaviors like violent injuries and falls, threatening their health and safety and requiring real-time technical monitoring. However, existing technolog...
A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Base...
Multispectral detection leverages visible and infrared imaging to improve detection performance in complex environments. However, conventional convolution-based fusion methods predominantly rely on local feature interactions, limiting their capacity...
Robust object detection in practical sensing systems remains challenging under low visibility, rapidly varying illumination, occlusion, and cluttered backgrounds. Visible images provide rich structural and texture information but are vulnerable to da...
Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object d...
Maritime small object detection is critical for UAV-based sea surveillance but remains challenging due to the small size of targets and interference from sea reflections and waves. This paper proposes SeaLSOD-YOLO, a lightweight detection algorithm b...
Fish diseases are one of the primary causes of economic losses in aquaculture. Existing deep learning models have progressed in fish disease detection and lesion segmentation. However, many models still have limitations, such as detecting only a sing...
Accurate and efficient fish detection in underwater environments is fundamental to the advancement of automated aquaculture monitoring and selective fishing systems. However, the deployment of existing object detection models in such environments rem...