Improved Mask R-CNN Research Articles

Human Detection Based on Improved Mask R-CNN

Human detection has been widely concerned by academic and industrial circles. The gradual maturity of deep learning framework further improves the accuracy and speed of detection. However, the relatively mature human detection methods cannot get accurate detection results because of the complex background, shooting angle and many limitations of human behaviour. Aiming at solving the problems of human detection in complex scenes, a novel human detection algorithm based on the improved mask R-CNN framework is proposed by implementing the leading research results of object detection through deep learning. The algorithm combines ResNet and FPN to extract the features of the image, and then takes advantage of RoIAlign and fine-grained Slic to proofread the pixels. In the experiments that compared with the original mask R-CNN algorithm on the same data set was carried out to verify the effectiveness of the proposed algorithm. The mAP and AR value of the improved mask R-CNN algorithm is greater than that of the mask R-CNN algorithm when the IoU is 0.5-0.95, showing that the improved mask R-CNN framework was able to detect human from video better.

Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN

This study presents a simple and effective Mask R-CNN algorithm for more rapid detection of vehicles and pedestrians. The method is of practical value for anticollision warning systems in intelligent driving. Deep neural networks with more layers have greater capacity but also have to perform more complicated calculations. To overcome this disadvantage, this study adopts a Resnet-86 network as a backbone that differs from the backbone structure of Resnet-101 in the Mask R-CNN algorithm within practical conditions. The results show that the Resnet-86 network can reduce the operation time and greatly improve accuracy. The detected vehicles and pedestrians are also screened out based on the Microsoft COCO dataset. The new dataset is formed by screening and supplementing COCO dataset, which makes the training of the algorithm more efficient. Perhaps, the most important part of our research is that we propose a new algorithm, Side Fusion FPN. The parameters in the algorithm have not increased, the amount of calculation has increased by less than 0.000001, and the mean average precision (mAP) has increased by 2.00 points. The results show that, compared with the algorithm of Mask R-CNN, our algorithm decreased the weight memory size by 9.43%, improved the training speed by 26.98%, improved the testing speed by 7.94%, decreased the value of loss by 0.26, and increased the value of mAP by 17.53 points.

Instance Segmentation Method Based on Improved Mask R-CNN for the Stacked Electronic Components

Object-detection methods based on deep learning play an important role in achieving machine automation. In order to achieve fast and accurate autonomous detection of stacked electronic components, an instance segmentation method based on an improved Mask R-CNN algorithm was proposed. By optimizing the feature extraction network, the performance of Mask R-CNN was improved. A dataset of electronic components containing 1200 images (992 × 744 pixels) was developed, and four types of components were included. Experiments on the dataset showed the model was superior in speed while being more lightweight and more accurate. The speed of our model showed promising results, with twice that of Mask R-CNN. In addition, our model was 0.35 times the size of Mask R-CNN, and the average precision (AP) of our model was improved by about two points compared to Mask R-CNN.

Face Detection and Segmentation Based on Improved Mask R-CNN

Deep convolutional neural networks have been successfully applied to face detection recently. Despite making remarkable progress, most of the existing detection methods only localize each face using a bounding box, which cannot segment each face from the background image simultaneously. To overcome this drawback, we present a face detection and segmentation method based on improved Mask R-CNN, named G-Mask, which incorporates face detection and segmentation into one framework aiming to obtain more fine-grained information of face. Specifically, in this proposed method, ResNet-101 is utilized to extract features, RPN is used to generate RoIs, and RoIAlign faithfully preserves the exact spatial locations to generate binary mask through Fully Convolution Network (FCN). Furthermore, Generalized Intersection over Union (GIoU) is used as the bounding box loss function to improve the detection accuracy. Compared with Faster R-CNN, Mask R-CNN, and Multitask Cascade CNN, the proposed G-Mask method has achieved promising results on FDDB, AFW, and WIDER FACE benchmarks.

An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask R-CNN.

In this paper, we consider building extraction from high spatial resolution remote sensing images. At present, most building extraction methods are based on artificial features. However, the diversity and complexity of buildings mean that building extraction methods still face great challenges, so methods based on deep learning have recently been proposed. In this paper, a building extraction framework based on a convolution neural network and edge detection algorithm is proposed. The method is called Mask R-CNN Fusion Sobel. Because of the outstanding achievement of Mask R-CNN in the field of image segmentation, this paper improves it and then applies it in remote sensing image building extraction. Our method consists of three parts. First, the convolutional neural network is used for rough location and pixel level classification, and the problem of false and missed extraction is solved by automatically discovering semantic features. Second, Sobel edge detection algorithm is used to segment building edges accurately so as to solve the problem of edge extraction and the integrity of the object of deep convolutional neural networks in semantic segmentation. Third, buildings are extracted by the fusion algorithm. We utilize the proposed framework to extract the building in high-resolution remote sensing images from Chinese satellite GF-2, and the experiments show that the average value of IOU (intersection over union) of the proposed method was 88.7% and the average value of Kappa was 87.8%, respectively. Therefore, our method can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy.

Research on Detection of Large Coal Blockage at the Transfer Point of Belt Conveyor Based on Improved Mask R-CNN

Aiming at the problem of coal accumulation and blockage at the transfer point at the transfer point due to the presence of large coal in the coal belt conveyor, a method based on the improved Mask R-CNN for the detection of the jam at the transfer point of the belt conveyor is proposed. The method firstly adds the SENET and SKNet models to the Mask R-CNN feature extraction part ResNet50, enhances the function of extracting features, and cancels the segmentation part of Mask R-CNN. In order to make the network better converge, the loss function in the RPN network is optimized, and the original IOU is replaced with GIOU, which solves the problem that the IOU cannot be optimized when it is 0 and the IOU cannot distinguish the anchor from the ground truth.

Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN

Traffic congestion due to vehicular accidents seriously affects normal travel, and accurate and effective mitigating measures and methods must be studied. To resolve traffic accident compensation problems quickly, a vehicle-damage-detection segmentation algorithm based on transfer learning and an improved mask regional convolutional neural network (Mask RCNN) is proposed in this paper. The experiment first collects car damage pictures for preprocessing and uses Labelme to make data set labels, which are divided into training sets and test sets. The residual network (ResNet) is optimized, and feature extraction is performed in combination with Feature Pyramid Network (FPN). Then, the proportion and threshold of the Anchor in the region proposal network (RPN) are adjusted. The spatial information of the feature map is preserved by bilinear interpolation in ROIAlign, and different weights are introduced in the loss function for different-scale targets. Finally, the results of self-made dedicated dataset training and testing show that the improved Mask RCNN has better Average Precision (AP) value, detection accuracy and masking accuracy, and improves the efficiency of solving traffic accident compensation problems.

Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images

In recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method based on an improved Mask R-CNN model. Our proposed method can accurately detect and segment ships at the pixel level. By adding a bottom-up structure to the FPN structure of Mask R-CNN, the path between the lower layers and the topmost layer is shortened, allowing the lower layer features to be more effectively utilized at the top layer. In the bottom-up structure, we use channel-wise attention to assign weights in each channel and use the spatial attention mechanism to assign a corresponding weight at each pixel in the feature maps. This allows the feature maps to respond better to the target's features. Using our method, the detection and segmentation mAPs increased from 70.6% and 62.0% to 76.1% and 65.8%, respectively.

Improved Mask R-CNN Segmentation for Contour Extraction of Individual Cattle from an Image

Forest species recognition has typically been approached as a texture classification problem and investigated by employing conventional texturing techniques like Local Convolutional Neural Network (CNN). Procedures have been the subject of current research for classification issues, with cutting-edge technique outcomes for object recognition and other activities, but are not yet commonly employed for texture problems. Several forest datasets, including one with macroscopic photos and another with microscopic images, are examined in this process to see how deep learning strategy, more specifically Convolutional Neural Networks (CNN), may be used to classify textures. Given the better resolution photos of these issues, we provide a strategy that can deal with them in order to get excellent accuracy and steer clear of the load of training and constructing an architecture with a lot of free parameters. On the first dataset, the suggested CNN-based technique superior to cutting-edge technology, which achieves an accuracy of 95.77%. It surpasses the best reported result of 93.2% on the dataset of microscopic pictures, achieving 97.32%.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

The cucumber fruits have the same color with leaves and their shapes are all long and narrow, which is different from other common fruits, such as apples, tomatoes, and strawberries, etc. Therefore, cucumber fruits are more difficult to be detected by machine vision in greenhouses for special color and shape. A pixel-wise instance segmentation method, mask region-based convolutional neural network (Mask RCNN) of an improved version, is proposed to detect cucumber fruits. Resnet-101 is selected as the backbone of Mask RCNN with feature pyramid network (FPN). To improve the detection precision, region proposal network (RPN) in original Mask RCNN is improved. Logical green ( LG ) operator is designed to filter non-green background and limit the range of anchor boxes. Besides, the scales and aspect ratios of anchor boxes are also adjusted to fit the size and shape of fruits. Improved Mask RCNN has a better performance on test images. The test results are compared with that of original Mask RCNN, Faster RCNN, you only look once (YOLO) V2 and YOLO V3. The $F_{1}$ score of improved Mask RCNN in test results reaches 89.47%, which is higher than the other methods. The average elapsed time of improved Mask RCNN is 0.3461 s, which is only lower than the original Mask RCNN. Meanwhile, the mean value and standard deviation of location deviation in improved Mask RCNN are 2.10 pixels and 1.73 pixels respectively, which are lower than the other methods.