A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

The accurate detection of green citrus in natural environments is a key step in realizing the intelligent harvesting of citrus through robotics. At present, the visual detection algorithms for green citrus in natural environments still have poor accuracy and robustness due to the color similarity between fruits and backgrounds. This study proposed a multi-scale convolutional neural network (CNN) named YOLO BP to detect green citrus in natural environments. Firstly, the backbone network, CSPDarknet53, was trimmed to extract high-quality features and improve the real-time performance of the network. Then, by removing the redundant nodes of the Path Aggregation Network (PANet) and adding additional connections, a bi-directional feature pyramid network (Bi-PANet) was proposed to efficiently fuse the multilayer features. Finally, three groups of green citrus detection experiments were designed to evaluate the network performance. The results showed that the accuracy, recall, mean average precision (mAP), and detection speed of YOLO BP were 86, 91, and 91.55% and 18 frames per second (FPS), respectively, which were 2, 7, and 4.3% and 1 FPS higher than those of YOLO v4. The proposed detection algorithm had strong robustness and high accuracy in the complex orchard environment, which provides technical support for green fruit detection in natural environments.

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3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽

10.1109/access.2019.2955995 ◽

2019 ◽

Vol 7 ◽

pp. 171461-171470

Author(s):

Dianwei Wang ◽

Yanhui He ◽

Ying Liu ◽

Daxiang Li ◽

Shiqian Wu ◽

...

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Algorithm ◽

3D Object ◽

Multi Scale ◽

Panoramic Images ◽

3D Object Detection

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Image target detection algorithm compression and pruning based on neural network

Computer Science and Information Systems ◽

10.2298/csis200316007s ◽

2021 ◽

Vol 18 (2) ◽

pp. 499-516

Author(s):

Yan Sun ◽

Zheping Yan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Target Detection ◽

Network Model ◽

Detection Algorithm ◽

Image Feature ◽

Propagation Mode ◽

Learning Technology ◽

Image Detection ◽

Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

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Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

Pertanika Journal of Science and Technology ◽

10.47836/pjst.28.s2.13 ◽

2020 ◽

Vol 28 (S2) ◽

Author(s):

Asmida Ismail ◽

Siti Anom Ahmad ◽

Azura Che Soh ◽

Mohd Khair Hassan ◽

Hazreen Haizi Harith

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection System ◽

Object Classification ◽

Detection Algorithm ◽

Learning Object ◽

Sliding Windows ◽

Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

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An efficient fire detection algorithm based on multi‐scale convolutional neural network

Fire and Materials ◽

10.1002/fam.3045 ◽

2021 ◽

Author(s):

Yanying Cheng ◽

Ke Chen ◽

Hui Bai ◽

Chunjie Mou ◽

Yuchun Zhang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Fire Detection ◽

Detection Algorithm ◽

Multi Scale

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Integration Between Cascade Region-Based Convolutional Neural Network and Bi-Directional Feature Pyramid Network for Live Object Tracking and Detection

Traitement du signal ◽

10.18280/ts.380437 ◽

2021 ◽

Vol 38 (4) ◽

pp. 1253-1257

Author(s):

Lehai Zhong ◽

Jiao Li ◽

Feifan Zhou ◽

Xiaoan Bao ◽

Weiyin Xing ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Object Tracking ◽

Video Frame ◽

Single Shot ◽

Detection Model ◽

Detection Algorithms ◽

Feature Pyramid ◽

Small Targets ◽

Detection Effect

The current target tracking and detection algorithms often have mistakes and omissions when the target is occluded or small. To overcome the defects, this paper integrates bi-directional feature pyramid network (BiFPN) into cascade region-based convolutional neural network (R-CNN) for live object tracking and detection. Specifically, the BiFPN structure was utilized to connect between scales and fuse weighted features more efficiently, thereby enhancing the network’s feature extraction ability, and improving the detection effect on occluded and small targets. The proposed method, i.e., Cascade R-CNN fused with BiFPN, was compared with target detection algorithms like Cascade R-CNN and single shot detection (SSD) on a video frame dataset of wild animals. Our method achieved a mean average precision (mAP) of 91%, higher than that of SSD and Cascade R-CNN. Besides, it only took 0.42s for our method to detect each image, i.e., the real-time detection was realized. Experimental results prove that the proposed live object tracking and detection model, i.e., Cascade R-CNN fused with BiFPN, can adapt well to the complex detection environment, and achieve an excellent detection effect.

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Improved stereo matching algorithm based on multi-scale fusion

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213940876 ◽

2021 ◽

Vol 39 (4) ◽

pp. 876-882

Author(s):

Xing Chen ◽

Wenhai Zhang ◽

Yu Hou ◽

Lin Yang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Stereo Matching ◽

Dynamic Programming Algorithm ◽

Good Effect ◽

Disparity Map ◽

Data Set ◽

Matching Algorithm ◽

Multi Scale ◽

Feature Pyramid

Aiming at the low matching accuracy of local stereo matching algorithm in weak texture or discontinuous disparity areas, a stereo matching algorithm combining multi-scale fusion of convolutional neural network (CNN) and feature pyramid structure (FPN) is proposed. The feature pyramid is applied on the basis of the convolutional neural network to realize the multi-scale feature extraction and fusion of the image, which improves the matching similarity of the image blocks. The guide graph filter is used to quickly and effectively complete the cost aggregation. The disparity selection stage adapts the improvement dynamic programming algorithm to obtain the initial disparity map. The initial disparity map is refined so as to obtain the final disparity map. The algorithm is trained and tested on the image provided by Middlebury data set, and the result shows that the disparity map obtained by the algorithm has good effect.

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Dim Target Detection Method Based on Deep Learning in Complex Traffic Environment

10.21203/rs.3.rs-177944/v1 ◽

2021 ◽

Author(s):

Hao Zheng ◽

Jianfang Liu ◽

Xiaogang Ren

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Recognition Performance ◽

Vehicle Detection ◽

Optimization Method ◽

Multi Scale ◽

Traffic Environment ◽

Feature Pyramid ◽

Detection And Recognition

Abstract Although the current vehicle detection and recognition framework based on deep learning has its own characteristics and advantages, it is difficult to effectively combine multi-scale and multi category vehicle features, and there is still room for improvement in vehicle detection and recognition performance. Based on this, an improved fast R-CNN convolutional neural network is proposed to detect dim targets in complex traffic environment. The deep learning model of fast R-CNN convolutional neural network is introduced into the image recognition of complex traffic environment, and a structure optimization method is proposed, which replaces vgg16 in fast RCNN with RESNET to make it suitable for small target recognition in complex background. Max pooling is the down sampling method, and then feature pyramid network is introduced into RPN to generate target candidate box to optimize the structure of convolutional neural network. After training with 1497 images, the complex traffic environment images are identified and tested.

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