GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection

Liao, Jiajia; Liu, Yujun; Piao, Yun-Song; Su, Jinhe; Cai, Guorong; Wu, Yundong

doi:10.1007/s44196-021-00056-3

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…This has been confirmed in previous studies carried out by many scholars. Liao et al [ 42 ] presented a global and local ensemble network for objects in aerial images; the strengths and weaknesses of Yolov5 and CenterNet were fully considered, and the accuracy of the ensemble model constructed was significantly improved over the previous model. However, as mentioned earlier, for aerial images, different flight altitudes may lead to different detection results for the model, which is something missing in their study.…”

Section: Discussionmentioning

confidence: 99%

An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

Zhang

Zhao

2023

Sensors

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Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R2 of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information.

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Section: Discussionmentioning

confidence: 99%

An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

Zhang

Zhao

2023

Sensors

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“…GLE-Net (Global and local ensemble network) is proposed to detect the small, dense objects of aerial images taken by the drone. This network used the two base networks YOLOv5 and CenterNet for comparison and this network can act as plug-and-play network for improving the accuracy of any detection network [ 92 ]. Some of the objects such as golf courses, sewage treatment plants, and airports that are not present in fixed size or shape.…”

Section: Applications Of Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

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Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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“…CenterNet [10] is also a kind of one-stage method, in which an object is detected according to one center key point and two key points of a bounding box, which contains the center location and other attributes of an object (e.g., size). Tis model has been used for aerial object detection by combining with the Yolov5 model [15].…”

Section: Related Workmentioning

confidence: 99%

“…Te integrated module usually considers the character of the object to improve the whole model's capability, which has been used in many scenarios since it combines the decision of multiple submodules to upgrade the overall performance. Tese approaches have been efectively employed for improving accuracy in some object detection tasks [15]. Unfortunately, regarding the complexity and confguration of deep learning-based object detection models, it is not a simple process of incorporating a reasonable submodule to improve the detection performance.…”

Section: Introductionmentioning

confidence: 99%

Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism

Wei

Dong

Wang

2022

Journal of Electrical and Computer Engineering

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Petrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the missing and false detection of equipment with extreme sizes, due to image quality, equipment scale, light, and other factors. In this paper, a one-stage attention mechanism-enhanced Yolov5 network is proposed to detect typical types of petrochemical equipment in industry scene images. The model considers the advantages of the channel and spatial attention mechanism and incorporates it into the three mainframes. Furthermore, the multiscale deep feature is fused with a bottom-up feature pyramid structure to learn the features of equipment with extreme sizes. Moreover, an adaptive anchor generation algorithm is proposed to handle objects with extreme sizes in a complex background. In addition, the data augmentation strategy is also introduced to handle the relatively small and extremely large sample and to enhance the robustness of the fused model. The proposed model was validated on the self-built petrochemical equipment image data set, and the experimental results show that it achieves a competitive performance in comparison with the related state-of-the-art detectors.

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GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection

Cited by 8 publications

References 36 publications

An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

Tools, techniques, datasets and application areas for object detection in an image: a review

Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism

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