The goal of this paper is to introduce a semantic segmentation neural network designed for the detection of firearms. The proposed network applies a fully convolutional architecture, incorporating features such as ski...
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ISBN:
(纸本)9783031425073;9783031425080
The goal of this paper is to introduce a semantic segmentation neural network designed for the detection of firearms. The proposed network applies a fully convolutional architecture, incorporating features such as skip connections and batch normalization to enhance its performance. The network was trained using a vast dataset of annotated images and its performance was evaluated using a separate dataset. The results show that the proposed network is highly effective, achieving top-notch results in the detection of firearms. The network's high accuracy 99.1% and ability to perform pixel-wise classification make it a valuable solution for real-world gun detection applications.
Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms. In previous research, we studied detecting plant parts by a sem...
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Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms. In previous research, we studied detecting plant parts by a semantic segmentation neural network. Additionally, in this research, we detect the pruning points of leaves in 3D space by using 3D point clouds. Robot arms can move to these positions and cut the leaves. We proposed a method to create 3D point clouds of sweet peppers by applying semantic segmentation neural networks, the ICP algorithm, and ORB-SLAM3, a visual SLAM application with a LiDAR camera. This 3D point cloud consists of plant parts that have been recognized by the neuralnetwork. We also present a method to detect the leaf pruning points in 2D images and 3D space by using 3D point clouds. Furthermore, the PCL library was used to visualize the 3D point clouds and the pruning points. Many experiments are conducted to show the method's stability and correctness.
Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. ...
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Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semanticsegmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neuralnetwork. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.
One or two RGB-D images cannot provide enough information to detect cut-off points in pruning systems. 3D semantic point clouds constructed from many RGB-D images represent real tomato plants and help the system find ...
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ISBN:
(纸本)9781665499248
One or two RGB-D images cannot provide enough information to detect cut-off points in pruning systems. 3D semantic point clouds constructed from many RGB-D images represent real tomato plants and help the system find the cut-off points correctly. We proposed a method to create 3D semantic point clouds based on ORB-SLAM3, ICP (iterative closet point) algorithm, and semantic segmentation neural network. RGB-D images are converted to semantic images by the semantic segmentation neural network. Each pair of camera poses which is estimated by ORB-SLAM3, and an RGB-D semantic image is used to create a 3D point cloud. The ICP method is applied to stick and refine these point clouds to construct a full 3D semantic point cloud.
Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a metho...
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Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neuralnetwork to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.
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