Large scale instance segmentation of outdoor environment based on improved YOLACT

Zhao, Zichen; Tong, Xiliang; Sun, Ying; Bai, Dongxu; Liu, Xin; Zhao, Guangbo; Fan, Hanwen; Li, Jun; Zou, Cejing; Chen, Baojia

doi:10.1002/cpe.7370

Cited by 4 publications

(4 citation statements)

References 110 publications

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“…Finally, exploring how alternative instance segmentation algorithms, such as the YOLO series could be an interesting avenue of research 63 . This particular algorithm has shown remarkable results in previous studies and could offer valuable insights into better ways of characterizing land features from elevation models 64 – 66 .…”

Section: Discussionmentioning

confidence: 92%

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data

Bilodeau,

Esau,

Zaman

et al. 2024

Sci Rep

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Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries delineation and classification methods, such as manual digitalization and traditional segmentation techniques, are labour-intensive and often require manual and time-consuming parameter selection. In recent years, deep learning (DL) techniques, including convolutional neural networks and Mask R-CNN, have shown promising results in object recognition, image classification, and segmentation tasks. However, there is a gap in applying these techniques to detecting surface drainage patterns on agricultural fields. This paper develops and tests a Mask R-CNN model for detecting land-formed fields on agricultural dykelands using LiDAR-derived elevation data. Specifically, our approach focuses on identifying groups of pixels as cohesive objects within the imagery, a method that represents a significant advancement over pixel-by-pixel classification techniques. The DL model developed in this study demonstrated a strong overall performance, with a mean Average Precision (mAP) of 0.89 across Intersection over Union (IoU) thresholds from 0.5 to 0.95, indicating its effectiveness in detecting land-formed fields. Results also revealed that 53% of Nova Scotia’s dykelands are being used for agricultural purposes and approximately 75% (6924 hectares) of these fields were land-formed. By applying deep learning techniques to LiDAR-derived elevation data, this study offers novel insights into surface drainage mapping, enhancing the capability for precise and efficient agricultural land management in regions vulnerable to environmental changes.

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Section: Discussionmentioning

confidence: 92%

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data

Bilodeau,

Esau,

Zaman

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…To solve the above problems, the main methods available are to reduce the number of convolution cores or to fuse images in multiple sources to increase the extraction of effective features. However, the former is suitable for situations where the background of image data is highly controllable, such as indoor environment or image data collected through a standardization process, but not for complex field environments Zhao et al, 2021;Bai et al, 2022).…”

Section: Feature Fusion Methodsmentioning

confidence: 99%

“…The spatial attention module used convolution and the sigmoid function to process the input feature map and ultimately determined where to pay attention. Compared with separate channel attention network SENet and spatial attention network STN, the CBAM does not increase too much computation ( Zhao et al., 2022 ). It is a lightweight module that can be integrated into the most well-known CNN architecture and can be trained in an end-to-end manner.…”

Section: Methodsmentioning

confidence: 99%

Real-time determination of flowering period for field wheat based on improved YOLOv5s model

et al. 2023

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The flowering period is one of the important indexes of wheat breeding. The early or late flowering affects the final yield and character stability of wheat. In order to solve the problem that it is difficult to accurately and quickly detect the flowering period of a large number of wheat breeding materials, a determination method of flowering period for field wheat based on the improved You Only Look Once (YOLO) v5s model was proposed. Firstly, a feature fusion (FF) method combing RGB images and corresponding comprehensive color features was proposed to highlight more texture features and reduce the distortion caused by light on the extracted feature images. Second, the YOLOv5s model was selected as a base version of the improved model and the convolutional block attention model (CBAM) was adopted into the feature fusion layer of YOLOV5s model. Florets and spikelets were given greater weight along the channel and spatial dimensions to further refine their effective feature information. At the same time, an integrated Transformer small-target detection head (TSDH) was added to solve the high miss rate of small targets in wheat population images. The accurate and rapid detection of florets and spikelets was realized, and the flowering period was determined according to the proportion of florets and spikelets. The experimental results showed that the average computing time of the proposed method was 11.5ms, and the average recognition accuracy of florets and spikelets was 88.9% and 96.8%, respectively. The average difference between the estimated flowering rate and the actual flowering rate was within 5%, and the determination accuracy of the flowering period reached 100%, which met the basic requirements of the flowering period determination of wheat population in the field.

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“…Experimental study of the control algorithm by combining the hardware and software of the manipulator. An experimental system for the motion trajectory of the manipulator is set up to experiment with the control algorithm designed for adaptive fuzzy SMC of the robot trajectory tracking 92–94 …”

Section: Dobot Magician Manipulator Experimentsmentioning

confidence: 99%

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Zhao

Tao

et al. 2023

Concurrency and Computation

Self Cite

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Sliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control method.

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Large scale instance segmentation of outdoor environment based on improved YOLACT

Cited by 4 publications

References 110 publications

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data

Real-time determination of flowering period for field wheat based on improved YOLOv5s model

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

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