Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu, Shanwei; Nie, Zhigang; Li, Guang; Zhu, Wenyu

doi:10.3390/drones8050170

Cited by 6 publications

(1 citation statement)

References 38 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) The key points in the Head part and the decoupling information of key points are optimized through the coordinate attention mechanism in order to solve the problems of complex target background and poor target occlusion detection accuracy and to improve the positioning accuracy of key points. (4) The loss function and confidence function are improved to guarantee the robustness of the projection of the bounding box (BBox) for human pose estimation in complex scenes in order to improve the robustness of the model and prevent the occurrence of lagging, frame dropping, and video blurring problems [14].…”

Section: Contribution Of This Articlementioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement.

show abstract

Section: Contribution Of This Articlementioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s

Yan,

Liu,

Yan

2024

Agronomy

View full text Add to dashboard Cite

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture’s design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot.

show abstract

YOMO-Runwaynet: A Lightweight Fixed-Wing Aircraft Runway Detection Algorithm Combining YOLO and MobileRunwaynet

Dai,

Zhai,

Wang

et al. 2024

Drones

View full text Add to dashboard Cite

The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a lightweight attention module is introduced into MnasNet, and the improved MobileNetV3 is employed as the backbone network to enhance the feature extraction efficiency. Then, PANet and SPPnet are incorporated to aggregate the features from multiple effective feature layers. Subsequently, to reduce latency and improve efficiency, YOMO-Runwaynet generates a single optimal prediction for each object, eliminating the need for non-maximum suppression (NMS). Finally, experimental results on embedded devices demonstrate that YOMO-Runwaynet achieves a detection accuracy of over 89.5% on the ATD (Aerovista Runway Dataset), with a pixel error rate of less than 0.003 for runway keypoint detection, and an inference speed exceeding 90.9 FPS. These results indicate that the YOMO-Runwaynet algorithm offers high accuracy and real-time performance, providing effective support for the visual navigation of fixed-wing aircraft.

show abstract

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Cited by 6 publications

References 38 publications

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s

YOMO-Runwaynet: A Lightweight Fixed-Wing Aircraft Runway Detection Algorithm Combining YOLO and MobileRunwaynet

Contact Info

Product

Resources

About