scholarly journals Low Shot Box Correction for Weakly Supervised Object Detection

2019 ◽  
Author(s):  
Tianxiang Pan ◽  
Bin Wang ◽  
Guiguang Ding ◽  
Jungong Han ◽  
Junhai Yong
Keyword(s):  
Object Detection ◽  
Large Data ◽  
Detection Accuracy ◽  
Detection Problem ◽  
Data Set ◽  
Supervised Methods ◽  
Weakly Supervised ◽  
Bounding Boxes ◽  
State Of Art ◽  
Discriminative Parts

Weakly supervised object detection (WSOD) has been widely studied but the accuracy of state-of-art methods remains far lower than strongly supervised methods. One major reason for this huge gap is the incomplete box detection problem which arises because most previous WSOD models are structured on classification networks and therefore tend to recognize the most discriminative parts instead of complete bounding boxes. To solve this problem, we define a low-shot weakly supervised object detection task and propose a novel low-shot box correction network to address it. The proposed task enables to train object detectors on a large data set all of which have image-level annotations, but only a small portion or few shots have box annotations. Given the low-shot box annotations, we use a novel box correction network to transfer the incomplete boxes into complete ones. Extensive empirical evidence shows that our proposed method yields state-of-art detection accuracy under various settings on the PASCAL VOC benchmark.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals Extreme Learning Machine with sigmoid activation function on large data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3523-3526
Keyword(s):  
Efficient Algorithm ◽  
Large Data ◽  
Activation Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Learning Machine ◽  
Sigmoid Activation Function ◽  
State Of Art ◽  
Better Than

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

scholarly journals Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

2019 ◽  
Vol 11 (18) ◽  
pp. 2176 ◽  
Author(s):  
Chen ◽  
Zhong ◽  
Tan
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Detection Accuracy ◽  
Small Object ◽  
Data Set ◽  
Orientation Information

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

scholarly journals EFN: Field-Based Object Detection for Aerial Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3630
Author(s):  
Jin Liu ◽  
Haokun Zheng
Keyword(s):  
Object Detection ◽  
Semantic Segmentation ◽  
Natural Images ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Sets ◽  
Data Set ◽  
Bounding Box ◽  
Good Score ◽  
Bounding Boxes

Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images.

scholarly journals Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 10478-10485 ◽  
Author(s):  
Yingjie Cai ◽  
Buyu Li ◽  
Zeyu Jiao ◽  
Hongsheng Li ◽  
Xingyu Zeng ◽  
...  
Keyword(s):  
Object Detection ◽  
Depth Estimation ◽  
Physical World ◽  
Depth Information ◽  
Detection Accuracy ◽  
Unified Framework ◽  
3D Object ◽  
Depth Recovery ◽  
Bounding Boxes ◽  
3D Object Detection

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difficult on account of absence of depth information, this paper proposes a novel unified framework which decomposes the detection problem into a structured polygon prediction task and a depth recovery task. Different from the widely studied 2D bounding boxes, the proposed novel structured polygon in the 2D image consists of several projected surfaces of the target object. Compared to the widely-used 3D bounding box proposals, it is shown to be a better representation for 3D detection. In order to inversely project the predicted 2D structured polygon to a cuboid in the 3D physical world, the following depth recovery task uses the object height prior to complete the inverse projection transformation with the given camera projection matrix. Moreover, a fine-grained 3D box refinement scheme is proposed to further rectify the 3D detection results. Experiments are conducted on the challenging KITTI benchmark, in which our method achieves state-of-the-art detection accuracy.

scholarly journals CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator

2021 ◽  
Vol 12 (1) ◽  
pp. 281
Author(s):  
Jaesung Jang ◽  
Hyeongyu Lee ◽  
Jong-Chan Kim
Keyword(s):  
Object Detection ◽  
Real World ◽  
Driving Simulator ◽  
Data Extraction ◽  
Ground Truth ◽  
Autonomous Driving ◽  
Relevant Information ◽  
Detection Accuracy ◽  
Daunting Task ◽  
Bounding Boxes

For safe autonomous driving, deep neural network (DNN)-based perception systems play essential roles, where a vast amount of driving images should be manually collected and labeled with ground truth (GT) for training and validation purposes. After observing the manual GT generation’s high cost and unavoidable human errors, this study presents an open-source automatic GT generation tool, CarFree, based on the Carla autonomous driving simulator. By that, we aim to democratize the daunting task of (in particular) object detection dataset generation, which was only possible by big companies or institutes due to its high cost. CarFree comprises (i) a data extraction client that automatically collects relevant information from the Carla simulator’s server and (ii) a post-processing software that produces precise 2D bounding boxes of vehicles and pedestrians on the gathered driving images. Our evaluation results show that CarFree can generate a considerable amount of realistic driving images along with their GTs in a reasonable time. Moreover, using the synthesized training images with artificially made unusual weather and lighting conditions, which are difficult to obtain in real-world driving scenarios, CarFree significantly improves the object detection accuracy in the real world, particularly in the case of harsh environments. With CarFree, we expect its users to generate a variety of object detection datasets in hassle-free ways.

scholarly journals Weakly-supervised object detection via mining pseudo ground truth bounding-boxes

2018 ◽  
Vol 84 ◽  
pp. 68-81 ◽  
Author(s):  
Yongqiang Zhang ◽  
Yaicheng Bai ◽  
Mingli Ding ◽  
Yongqiang Li ◽  
Bernard Ghanem
Keyword(s):  
Object Detection ◽  
Ground Truth ◽  
Weakly Supervised ◽  
Bounding Boxes

scholarly journals Research on fine tuning parameter insulator identification based on YOLOV4 algorithm

2021 ◽  
Vol 2121 (1) ◽  
pp. 012041
Author(s):  
Meng Wang ◽  
Yuan Sun ◽  
Feize Xia
Keyword(s):  
Virtual Machine ◽  
Large Data ◽  
Fine Tuning ◽  
Tuning Parameter ◽  
Detection Accuracy ◽  
Aerial Photo ◽  
Data Set ◽  
Training Time ◽  
Tuning Parameters ◽  
Network Time

Abstract Aiming at the problem that the training network time of YOLOV4 algorithm is too long due to the large data set of aerial insulator images, a method based on YOLOV4 algorithm is proposed to shorten the training time by fine-tuning parameters without affecting the positioning detection accuracy. Based on the development of UBANTU virtual machine, through CUDA and CUDNN environment configuration, and through the detection and verification of insulator aerial photo data set, the feasibility of accurate positioning of insulators under the condition of fine tuning parameters of YOLOV4 algorithm is successfully proved.

scholarly journals Beyond Weakly Supervised: Pseudo Ground Truths Mining for Missing Bounding-Boxes Object Detection

2020 ◽  
Vol 30 (4) ◽  
pp. 983-997 ◽  
Author(s):  
Yongqiang Zhang ◽  
Mingli Ding ◽  
Yancheng Bai ◽  
Mengmeng Xu ◽  
Bernard Ghanem
Keyword(s):  
Object Detection ◽  
Weakly Supervised ◽  
Bounding Boxes

scholarly journals FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1356
Author(s):  
Suting Chen ◽  
Dongwei Shao ◽  
Xiao Shu ◽  
Chuang Zhang ◽  
Jun Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Large Scale ◽  
Detection Accuracy ◽  
Collaborative Network ◽  
Full Coverage ◽  
High Resolution Images ◽  
Multi Level ◽  
Weakly Supervised

With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a small number of high-resolution images with detailed object labels available, highly insufficient for common machine learning-based object detection algorithms. Another challenge is the huge range of object sizes: it is difficult to locate large objects, such as buildings and small objects, such as vehicles, simultaneously. To tackle these problems, we propose a novel neural network based remote sensing object detector called full-coverage collaborative network (FCC-Net). The detector employs various tailored designs, such as hybrid dilated convolutions and multi-level pooling, to enhance multiscale feature extraction and improve its robustness in dealing with objects of different sizes. Moreover, by utilizing asynchronous iterative training alternating between strongly supervised and weakly supervised detectors, the proposed method only requires image-level ground truth labels for training. To evaluate the approach, we compare it against a few state-of-the-art techniques on two large-scale remote-sensing image benchmark sets. The experimental results show that FCC-Net significantly outperforms other weakly supervised methods in detection accuracy. Through a comprehensive ablation study, we also demonstrate the efficacy of the proposed dilated convolutions and multi-level pooling in increasing the scale invariance of an object detector.

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