Development of a core feature identification application based on the Faster R-CNN algorithm

Jiang, Quan; Jia, Mingtao; Bi, Lin; Zheng, Zicheng; Gao, Ke

doi:10.1016/j.engappai.2022.105200

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…CNN possesses strong data-solving capabilities and can delve deeply into data relationship information [ 33 ]. As a network with multiple layers, CNN consists primarily of input layer, convolutional layer, down-sampling layer, fully connected layer, and output layer [ 34 ]. Its fundamental concept lies in the use of neuron weight sharing, which reduces the diversity of network parameters, simplifies the network, and enhances execution efficiency [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Liu,

Hua,

Xue

et al. 2023

Sensors

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The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

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Section: Methodsmentioning

confidence: 99%

Section: Weight Estimationmentioning

confidence: 99%

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Liu,

Hua,

Xue

et al. 2023

Sensors

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show abstract

“…The recognition effect was better. Lv et al [18] preprocessed the foreign object images on the conveyor belt and used the improved Faster-RCNN [19,20] model to achieve foreign object recognition on the conveyor belt. This model was of high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

et al. 2023

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During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we propose a lightweight target detection algorithm, GhostNet-CBAM-YOLOv4, to resolve the problem of the difficulty of detecting foreign objects at high-speed movement in an underground conveyor belt. The Kmeans++ clustering method was used to preprocess the data set to obtain the anchor box suitable for the foreign object size. The GhostNet lightweight module replaced the backbone network, reducing the model’s parameters. The CBAM attention module was introduced to enhance the ability of feature extraction facing the complex environment under the mine. The depth separable convolution was used to simplify the model structure and reduce the number of parameters and calculations. The detection accuracy of the improved method on the foreign body data set reached 99.32%, and the detection rate reached 54.7 FPS, which was 6.83% and 42.1% higher than the original YOLOv4 model, respectively. The improved method performed better than the original model on the other two datasets and could effectively avoid misdetection and omission detection. In comparison experiments with similar methods, our proposed method also demonstrated good performance, verifying its effectiveness.

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“…The target detection based on deep learning was developed for moving target detection to overcome the limitations of the traditional algorithm. In [2], the authors summed up target detection algorithms based on depth learning, including the faster region convolutional network (Faster R-CNN) algorithm [8][9][10], SSD algorithm [11,12], YOLO [13], YOLOv2 [14], YOLOv3 [15], YOLOv4 [16][17][18][19], YOLOv5 [20] algorithm, etc. It is also pointed out that both YOLO series and single-shot multiBox detector (SSD) algorithms follow the method of R-CNN series algorithms to perform classification pre-training on large datasets, and then fine-tune on small datasets.…”

Section: Introductionmentioning

confidence: 99%

Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm

et al. 2022

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For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method to constrain the sensitive area in the current detection image so as to improve the accuracy of water column detection; secondly, density-based spatial clustering of applications with noise (DBSCAN) + K-means clustering algorithm is used to obtain a better prior bounding box, which is input into the YOLOv4 network to improve the positioning accuracy of the water column; finally, the convolutional block attention module (CBAM) is added in the PANet structure to improve the detection accuracy of the water column. The experimental results show that the above algorithm can effectively improve the detection accuracy and positioning accuracy of the water column at the impact point.

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Development of a core feature identification application based on the Faster R-CNN algorithm

Cited by 12 publications

References 14 publications

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm

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