Domain-specific data augmentation for on-road object detection based on a deep neural network

The indoor scene object detection technology is of important research significance, which is one of the popular research topics in the field of scene understanding for indoor robots. In recent years, the solutions based on deep learning have achieved good results in object detection. However, there are still some problems to be further studied in indoor object detection methods, such as lighting problem and occlusion problem caused by the complexity of the indoor environment. Aiming at these problems, an improved object detection method based on deep neural networks is proposed in this article, which uses a framework similar to the single-shot multibox detector (SSD). In the proposed method, an improved ResNet50 network is used to enhance the transmission of information, and the feature expression capability of the feature extraction network is improved. At the same time, a multiscale contextual information extraction (MCIE) module is used to extract the contextual information of the indoor scene, so as to improve the indoor object detection effect. In addition, an improved dual-threshold non-maximum suppression (DT-NMS) algorithm is used to alleviate the occlusion problem in indoor scenes. Finally, the public dataset SUN2012 is further screened for the special application of indoor scene object detection, and the proposed method is tested on this dataset. The experimental results show that the mean average precision (mAP) of the proposed method can reach 54.10%, which is higher than those of the state-of-the-art methods.

At present, many cities have established sophisticated metro transportation systems, and metro passenger traffic accounts for about 50 to 80 percent of the total passenger traffic. Unfortunately, there are hundreds of incidents of train delays and even casualties caused by foreign objects in metros of various cities every year. The detection of foreign objects between metro doors and Platform Screen Doors (PSDs) has become an urgent problem to be solved. The main contribution of this paper is that this is the first attempt to use deep neural networks to solve the problem of foreign object detection between the PSDs and metro doors. In this paper, we first introduce the object detection framework called You Only Look Once (YOLO series) and Single Shot MultiBox Detector (SSD). We further explore the performance of YOLO v3 and SSD by implementing their models to do the foreign object detecting task in our dataset (from real-world data). The experimental results demonstrate that deep neural networks achieve good detection accuracy in the task of foreign object detection between the PSDs and metro doors. In addition, the deep neural network can also give the category information of foreign objects, which greatly reducing the time of artificial judgment and relieving traffic pressure. We hope our paper will serve as a solid baseline and help ease future research in foreign object detection between the PSDs and metro doors.

Advanced Driver Assistance System (ADAS) is a very important part of an up to date vehicle. For achieving highlevel objectives in such ADAS functionality like LKA (lane keeping assistance), LDW (lane departure warning system), FCW (forward collision warning) the quality of the algorithms under the hood must be extremely high. In the last few years, it is common that these algorithms are based on DNNs (deep neural networks) applied to the tasks of semantic and instance segmentation, 2D/3D object detection and visual object tracking. Recent state-of-the-art DNN models as usual solve only one single task from the listed above and running several neural networks is rather computationally expensive and even impossible due to the lack of the GPU memory. One of the approaches used to overcome such a problem is a shared backbone (also called feature extractor or encoder). The backbone consumes most of the computing resources thus the model with a shared backbone achieves better inference performance. Unfortunately, the training procedure for a shared backbone model has several difficulties. The first one is the lack of datasets with all the required and uniform annotation types. The second problem is a more sophisticated backpropagation procedure. In this paper, we consider several methods for multi-task neural network training and present the results of such training procedures on several public datasets with dissimilar annotation types. The shared backbone is applied to the following three tasks performed simultaneously on the road scene: semantic segmentation, 2D object detection and 3D object detection. While the performance of the DNNs with shared backbone increased significantly, we obtained the quality evaluation results, which are quite close to the original separate state-of-the-art DNNs and even outperforms them in some evaluation indices.

Data augmentation (DA) increases the diversity of training data to improve the model generalization ability. Most of the DA methods focus on image classification or object detection. Directly using the existing DA methods on change detection tasks not only falls short of fully exploring the specificity of the change image pairs but also leads to longer training times. In this article, we first propose a mask-guided mixing (MGM) DA for change detection, which mixes the change regions of the current training sample based on prediction results and labels to generate high-quality samples with more positive samples. We then propose a new reinforcement learning (RL)-based Adaptive DA method, AdaAug+, to adaptively select the optimal DA policy for the training samples. An actor selects the best augmentation operation from the operation set according to the image pair. The augmented image pairs make it easier for the change detector to learn the optimal parameters and improve the final detection performance. To reduce the training time, we identify and remove the redundant training samples during the training process by our redundancy searching policy. We have conducted various experiments on four remote sensing change detection datasets with different change detectors. The experimental results demonstrate that AdaAug+ achieves promising performance compared to the state-of-the-art DA methods and requires less training time.

In this paper, a high precision detection method of salient objects is presented based on deep convolutional networks with proper combinations of shallow and deep connections. In order to achieve better performance in the extraction of deep semantic features of salient objects, based on a symmetric encoder and decoder architecture, an upgrade of backbone networks is carried out with a transferable model on the ImageNet pre-trained ResNet50. Moreover, by introducing shallow and deep connections on multiple side outputs, feature maps generated from various layers of the deep neural network (DNN) model are well fused so as to describe salient objects from local and global aspects comprehensively. Afterwards, based on a holistically nested edge detector (HED) architecture, multiple fused side outputs with various sizes of receptive fields are integrated to form detection results of salient objects accordingly. A series of experiments and assessments on extensive benchmark datasets demonstrate the dominant performance of our DNN model for the detection of salient objects in accuracy, which has outperformed those of other published works.

3D Facial Expression Recognition (FER) is an active research topic due to its multi-fields human machine applications. We expose in this paper a new approach for Data Augmentation (DA) in order to improve 3D FER using Deep Neural Networks (DNN). Our main contribution consists in using the Coherent Point Drift (CPD) non-rigid registration to generate additional 3D facial data conveying various expressions mainly the prototypical expressions: Happiness, Sadness, Fear, Surprise, Disgust, and Anger. We start by choosing a set of different references defined by arbitrarily selected neutral faces. We apply then the CPD non-rigid registration between each selected neutral face and each 3D facial model conveying various expressions from the whole BU-3DFE database. Thus, we augment the dataset by a factor equal to the used references. Afterwards, we estimate the 3D elastic deformation between the reference (3D neutral face) and the target (3D face with expression) in order to generate consequently various 3D expressions by switching the reference and the target within the registration process. Afterwards, we gather the produced 3D expressions to increase the size of our dataset. Finally, we exploit a DNN architecture to evaluate our proposed DA method. The used DA is effective and increases our DNN performance. Experimental results operated on the whole BU-3DFE database shows promising results reaching 94.88%.

This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.

There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged. A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure. This paper provides the first in-depth investigation of the effect of different association and merging strategies. We compare different combinations of three spatial and two semantic affinity measures with four clustering methods for MC Dropout with a Single Shot Multi-Box Detector. Our results show that the correct choice of affinity-clustering combination can greatly improve the effectiveness of the classification and spatial uncertainty estimation and the resulting object detection performance. We base our evaluation on a new mix of datasets that emulate near open-set conditions (semantically similar unknown classes), distant open-set conditions (semantically dissimilar unknown classes) and the common closed-set conditions (only known classes).

The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer algorithms can apply the artistic style of one image to another image without changing the latter's high-level semantic content, which makes it feasible to employ neural style transfer as a data augmentation method to add more variation to the training dataset. The contribution of this paper is a thorough evaluation of the effectiveness of the neural style transfer as a data augmentation method for image classification tasks. We explore the state-of-the-art neural style transfer algorithms and apply them as a data augmentation method on Caltech 101 and Caltech 256 dataset, where we found around 2% improvement from 83% to 85% of the image classification accuracy with VGG16, compared with traditional data augmentation strategies. We also combine this new method with conventional data augmentation approaches to further improve the performance of image classification. This work shows the potential of neural style transfer in computer vision field, such as helping us to reduce the difficulty of collecting sufficient labelled data and improve the performance of generic image-based deep learning algorithms.

Object detection is crucial to extracting and analyzing information autonomously from high-resolution remote sensing images (HRSIs). To address ideal blocking for large-scale HRSI object detection, this study uses a novel adaptive orientation object-detection method for largescale HRSIs based on multi-scale block fusion. An adaptive orientation object-detection framework based on a convolutional neural network is applied to detect diverse objects of large-scale HRSIs through different block scales; average precision (AP) values of diverse object detection results are calculated at different block scales. Then, block scales matching the largest AP values of diverse objects are determined based on statistical results of the AP values of the diverse object at different block scales. Finally, object-detection results at block scales matching the largest AP values of diverse objects are fused by the non-maximum suppression algorithm to achieve large-scale HRSI object-detection results. Experimental findings reveal that the proposed method is better than any single block-scale object-detection method, resulting in satisfactory large-scale HRSI object-detection results.

With the rapid development of the autonomous vehicles industry, there has been a dramatic proliferation of research concerned with related works, where road markings detection is an important issue. When there is no public open data in a field, we must collect road markings data and label them by ourselves manually, which is huge labor work and takes lots of time. Moreover, object detection often encounters the problem of small object detection. The detection accuracy often decreases when the detection distance increases. This is primarily because distant objects on the road take up few pixels in the image and object scales vary depending on different distances and perspectives. For the sake of solving the issues mentioned above, this paper utilizes a virtual dataset and open dataset to train the object detection model and cross-field testing in the field of Taiwan roads. In order to make the model more robust and stable, the data augmentation method is employed to generate more data. Therefore, the data are increased through the data augmentation method and homography transformation of images in the limited dataset. Additionally, Inverse Perspective Mapping is performed on the input images to transform them into the bird's eye view, which solves the "small objects at far distance" problem and the "perspective distortion of objects" problem so that the model can clearly recognize the objects on the road. The model testing on the front-view images and bird's eye view images also shows a remarkable improvement of accuracy by 18.62%.

In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.

Deep learning methods for object detection in smart manufacturing: A survey

To alleviate the costly data annotation problem in deep learning-based object detection, we leverage the canonical view model for active sample selection to improve the effectiveness of learning. Inspired by the view-approximation model, we hypothesize that visual features learned from canonical views denote better representations of objects, thus boosting the effectiveness of object learning. We validate the hypothesis empirically in the context of robot learning for novel object detection. Based on this, we propose a novel on-line viewpoint exploration (OLIVE) method that (1) defines goodness-of-view by combining informativeness of visual features and consistency of model-based object detection, and (2) systematically explores and selects viewpoints to boost learning efficiency. Furthermore, we train a legacy Faster R-CNN model with a data augmentation method while leveraging data samples generated by the OLIVE pipeline. We test our method on the T-LESS dataset and show that the proposed method outperforms competitive benchmarking methods, especially when the samples are few.

Advanced driver assistance systems (ADASs) and autonomous vehicles are expected to increase safety, lower energy and fuel consumption, and lower pollutants from road traffic. The advanced driver assistance system’s major features include lane detection and tracking. Finding color line marks on the road is the technique of lane detection. The process of lane tracking aims to help the vehicle continue traveling along a predetermined course. Hence, automatic detection of lanes using convolutional neural networks (CNNs) models has gained popularity in the current economic development. This paper also aims at detecting various objects using convolutional neural networks. In this work, we offer object classification and detection, a demanding topic in computer vision and image processing. As a result, we deployed convolutional neural networks on the Keras platform with TensorFlow support. The experimental results illustrate the amount of time needed to train, test, and generate the model using a constrained computing environment. Here, we trained the system for about 80 images which have taken a couple of seconds to detect with better accuracy. Traffic sign recognition is carried out, which is a significant area of research in ADAS. It is crucial for driverless vehicles and is frequently used to read stationary or moving road signs along the side of the road. A comprehensive recognition system is made up of traffic sign detection (TSD) and categorization (TSC). The paper also aimed at traffic sign recognition, which is crucial to consider, because traffic sign recognition is typically applied to portable devices. The model’s detection accuracy is ensured as long as the speed is maintained. The model developed in this study is 99.89% accurate.