Improved Faster R-CNN Research Articles

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Traditionally height of end effector of pod pepper harvester is fixed, which induces it hardly adapt to growth height of clustered peppers. Firstly, aiming at the problems of small size and clustered growth of pepper fruits during identification task, an improved Faster R-CNN algorithm is proposed. On the one hand, strategies such as increasing the types and number of high-resolution anchors and using RoI Align instead of RoI Pooling are employed to improve the detection accuracy for tiny targets. On the other hand, ResNet+FPN instead of VGG16 and ResNet backbone structure is adopted as the low-level feature extractor, so extracting capability for small features can be enhanced effectively. Furthermore, to precisely locate the position of clustered peppers, a height calculation model combining the 2D image recognition results with its depth information is advanced. Comparative experiments show that the overall accuracy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> of our method reach 75.79% and 87.30%, respectively. Compared with VGG16 feature extraction model, the two indicators are improved by 8.7% and 1.3%, respectively. The small target detection accuracy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">small</sup> is increased about 11.4%, with recall rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AR</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">small</sup> increased up to 10.2%. The overall loss rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Loss</i> is reduced by 4.7%, which manifests greatly improvement compared to YOLOv3 model. The detection time of a single frame reaches 42 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ms</i> , which is slightly longer than that of YOLOv3 network, but it can still meet the real-time detection requirements of pepper harvester. In 3D location experiment, the average absolute height error of clustered peppers from the ground is 4.4 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> , that accounts to the relative average error of 1.1%, thus suffices the adjustment error requirement of the end effector.

Parts recognition method based on improved Faster RCNN

针对传统机器视觉检测识别方法受到装配零件之间相互遮挡、零件不同位姿、外部光照强度、小目标漏检影响，检测准确率不高的问题，提出了一种改进的Faster RCNN（region-based convolutional neural networks）零件识别方法。首先使用提取特征更好的ResNet101网络代替原始Faster RCNN模型中的VGG16特征提取网络；其次针对原始候选区域网络，增加2个新的锚点并重新设置候选框的纵横比，以得到15种尺寸不同的锚点；然后针对传统非极大值抑制（non-maximum suppression，NMS）方法因删除交并比大于阈值的候选框而出现漏检问题，使用Soft-NMS方法替换传统的NMS方法，从而减少密集区域漏检的情况；最后在训练模型阶段采用多尺度训练策略，降低漏检率，提高模型准确率。对零件的识别实验结果表明：改进后的Faster RCNN模型能够达到96.1%的精度，较原始模型提升了4.6%，可以满足光照较强、存在水渍干扰等较为复杂环境中零件的识别检测。

Adaptive Defect Detection for 3-D Printed Lattice Structures Based on Improved Faster R-CNN

To detect the internal small defects of three-dimensional (3D) printed lattice structural samples, this study proposes an adaptive defect detection method based on a faster region-based convolutional neural networks (Faster R-CNN) structure. A K-medoids clustering algorithm, which adopts the Manhattan distance to calculate clustering centers, is used to adaptively select the optimal preset anchors from 3D printed lattice structures obtained via a computed tomography slices dataset. Based on these preset anchors, an improved defect detection model for over-melt based on Faster R-CNN is constructed. To improve the generalization ability, data augmentation methods are used for the computed tomography slices and a fine-tuning strategy is used for Faster R-CNN. The experimental results show that the defects of the lattice structure can be effectively detected. The adaptive defect detection model achieves the expected average precision of 93.4%. The feasibility of the adaptive defect detection method is thereby verified.

Automatic dock identification based on improved Faster R-CNN

Automatic dock identification based on improved Faster R-CNN

Target Location Algorithm of Contact Network Pipe Cap Based on Improved Faster R-CNN

Target Location Algorithm of Contact Network Pipe Cap Based on Improved Faster R-CNN

The Improved Faster R-CNN for Detecting Small Facial Landmarks on Vietnamese Human Face Based on Clinical Diagnosis

The Improved Faster R-CNN for Detecting Small Facial Landmarks on Vietnamese Human Face Based on Clinical Diagnosis

Improved Faster R-CNN Based On CSP-DPN

For the feature extraction in deep-learning based model of object detection, although DenseNet has the strong ability of exploiting features, its learned feature may result in high redundancy. In contrast, ResNeXt can reduce the redundancy of feature effectively, however, it has the difficult to use high-level information to re-discover low-level features. In this paper, inspired by Dual Path Network (DPN), we proposed a new feature extraction strategy that make full use of DenseNet and ResNeXt to improve the feature extraction ability of original Faster R-CNN. In the feature extraction part, ResNeXt and DenseNet alternately take first half channels and second half channels of feature map to perform feature fusion, which can learn more comprehensive information and reduce model’s computational complexity. In addition, we also apply the Switchable Atrous Convolution (SAC) in Faster RCNN to enlarge receptive field, which can further improve the accuracy of the object detection. We evaluated our proposed model on coco2014 dataset, the experimental results show that we achieved about 9% higher of mAP than original DPN92-based Faster R-CNN.

A Method of Improved Faster R-CNN for Detecting Internal Defect of Metal Lattice Structure Based on Super-resolution Reconstruction

A Method of Improved Faster R-CNN for Detecting Internal Defect of Metal Lattice Structure Based on Super-resolution Reconstruction

Improved Faster R-CNN-Based Anomaly Target Detection for Truck Driving Environment

With the development of the economy and the improvement of people's living standards, the volume of goods transported is constantly on the rise. At present, the dominant position in the transport industry is still road transport, and at the same time the problem of road traffic accidents is becoming increasingly prominent. Generally speaking, the truck is relatively large, with a wide blind spot and high cab height, which makes the driver unable to have a good observation and control of the environment around the vehicle, so it is extremely easy to have all kinds of traffic accidents. In such a context, an improved Faster R-CNN algorithm is proposed for the detection of abnormal targets in the truck driving environment. First, the ResNet-50 network is chosen to replace the VGG network, which reduces the training difficulty and effectively improves the gradient disappearance problem; then, in order to enhance the feature extraction ability of the network, the Squeeze-and-Excitation attention mechanism is introduced in the residual structure to strengthen the feature extraction ability; finally, the original feature pyramid network structure is improved by adding a self Finally, the original feature pyramid structure was improved by adding a bottom-up channel enhancement route to improve the propagation of lower-level features and further enhance the propagation of feature information. The experimental results show that the improved mAP value improves by 2.05% compared to the original algorithm.

Semi-Supervised SAR Target Detection Based on an Improved Faster R-CNN

In the remote sensing image processing field, the synthetic aperture radar (SAR) target-detection methods based on convolutional neural networks (CNNs) have gained remarkable performance relying on large-scale labeled data. However, it is hard to obtain many labeled SAR images. Semi-supervised learning is an effective way to address the issue of limited labels on SAR images because it uses unlabeled data. In this paper, we propose an improved faster regions with CNN features (R-CNN) method, with a decoding module and a domain-adaptation module called FDDA, for semi-supervised SAR target detection. In FDDA, the decoding module is adopted to reconstruct all the labeled and unlabeled samples. In this way, a large number of unlabeled SAR images can be utilized to help structure the latent space and learn the representative features of the SAR images, devoting attention to performance promotion. Moreover, the domain-adaptation module is further introduced to utilize the unlabeled SAR images to promote the discriminability of features with the assistance of the abundantly labeled optical remote sensing (ORS) images. Specifically, the transferable features between the ORS images and SAR images are learned to reduce the domain discrepancy via the mean embedding matching, and the knowledge of ORS images is transferred to the SAR images for target detection. Ultimately, the joint optimization of the detection loss, reconstruction, and domain adaptation constraints leads to the promising performance of the FDDA. The experimental results on the measured SAR image datasets and the ORS images dataset indicate that our method achieves superior SAR target detection performance with limited labeled SAR images.

Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images.

This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrared images is proposed. After discussing factors that affect sample diversity, background, object shape, and gray scale distribution are established as three key variables for synthesis. Raw infrared images without fault are used as backgrounds. By simulating the other two key variables on the background, different classes of objects are synthesized. To improve the detection rate of small scale targets, an attention module is introduced in the network to fuse the semantic segment results of U-Net and the synthetic dataset. In this way, the Faster R-CNN can obtain rich representation ability about small scale object on the infrared images. Strategies of parameter tuning and transfer learning are also applied to improve the detection precision. The detection system trains on only synthetic dataset and tests on actual images. Extensive experiments on different infrared datasets demonstrate the effectiveness of the synthetic methods. The synthetically trained network obtains a mAP of 0.826, and the recall rate of small latent short circuit is superior to that of Faster R-CNN and U-Net, effectively avoiding short-circuit missed detection.

Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.

Face mask detection algorithm for multi task recognition in complex environment

The right wearing masks can effectively prevent the spread of viruses. There are usually complex distractions in densely populated environments, which will affect detection tasks of face masks. However, the current detecting algorithm based on Faster R-CNN can not meet mask wearing detection of small targets in complex environments, so this paper puts forward a mask wearing detection algorithm based on the improved Faster R-CNN. This algorithm changes the traditional single RPN network model into one multi-task enhanced RPN model, which can improve the detection and recognition accuracy, the improved Soft-NMS algorithm is used to delete the redundant candidate boxes of regional candidate network output. At the same time, CIoU loss function with better performance, which fully considers the center point distance between the target and the detection frame and improves the accuracy of detection, replaced the original IoU loss function. 3000 sample images are used to do training and experimental verification of the model according to 1:3ratio of positive and negative samples. Experimental results show that compared with the Faster R-CNN algorithm, the algorithm proposed in this paper improves the face detection accuracy and face mask wearing detection by 7.15% and 15.99%respectively. Besides, the average detection accuracy improved by 11.57% and the FPS by 4.6.

Textile Fabric Defect Detection Based on Improved Faster R-CNN

To identify and locate industrial textile defects accurately, this study proposes a textile detection model based on a convolution neural network (CNN) known as Faster R-CNN. First, a textile defect feature map was extracted by ResNet-101 deep convolution network. Faster R-CNN only extracts features from the last layer of the feature map, which leads to a loss of low-level location information. The proposed method adds the feature pyramid network (FPN) to the network architecture to make an independent prediction for each level in the feature extraction stage. The extracted feature map is input into the regional proposal network, among which the overlapping regional proposals are suppressed. The proposed improved Faster R-CNN model with Region Proposal Network (RPN), Soft Non-Maximum Suppression (NMS), and Region of Interest (ROI) Align can achieve a detection accuracy of 98% and an mean of Average Precision (mAP) of 85%, which is more competitive than the state-of-the-art deep learning-based object detection algorithms.

A self-attention based faster R-CNN for polyp detection from colonoscopy images

At present, the incidence rate of colorectal cancer (CRC) is increasing year by year. It has always affected people's physical and mental health and quality of life. How to improve the detection ability of polyp plays a key role in colonoscopy. In order to solve these problems, in this paper, we first enhance the contrast of the input image by well distinguishing the foreground from the background in order to improve the saliency of the polyp regions. Then, we feed the enhanced image into an improved Faster R-CNN architecture comprised three processing modules for feature extraction, region proposal generation, and polyp detection, respectively. In order to further improve the quality, as well as the feature abstraction capability of the feature maps produced by the feature extraction network, we append an attention module to attend to the useful feature channels and weaken the contributions of the helpless feature channels. The experimental results demonstrate that the accuracy of the proposed polyp detection network is greatly improved compared with the existing algorithms, and the network not only can accurately identify polyps of varying sizes and conditions in single polyp images, but also can achieve excellent performance in handling multiple polyp images. This paper will be greatly helpful to alleviate the missed diagnosis of clinicians in the process of endoscopic examination and disease treatment, as well as providing effective assistance for the early diagnosis, treatment and prevention of the CRC, which is also of great significance to the clinical work of physicians.

Fabric Defect Detection based on Improved Faster RCNN

In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.

Esophageal cancer detection based on classification of gastrointestinal CT images using improved Faster RCNN

PurposeEsophageal cancer is a common malignant tumor in life, which seriously affects human health. In order to reduce the work intensity of doctors and improve detection accuracy, we proposed esophageal cancer detection using deep learning. The characteristics of deep learning: association and structure, activity and experience, essence and variation, migration and application, value and evaluation. MethodThe improved Faster RCNN esophageal cancer detection in this paper introduces the online hard example mining (OHEM) mechanism into the system, and the experiment used 1520 gastrointestinal CT images from 421 patients. Then, we compare the overall performance of Inception-v2, Faster RCNN, and improved Faster RCNN through F-1 measure, mean average precision (mAP), and detection time. ResultsThe experiment shows that the overall performance of the improved Faster RCNN is higher than the other two networks. The F-1 measure of our method reaches 95.71%, the mAP reaches 92.15%, and the detection time per CT is only 5.3s. ConclusionThrough comparative analysis on the esophageal cancer image data set, the experimental results show that the introduction of online hard example mining mechanism in the Faster RCNN algorithm can improve the detection accuracy to a certain extent.