scholarly journals Object Detection Using Stacked YOLOv3

2020 ◽  
Vol 25 (5) ◽  
pp. 691-697
Author(s):  
Sai Shilpa Padmanabula ◽  
Ramya Chowdary Puvvada ◽  
Venkatramaphanikumar Sistla ◽  
Venkata Krishna Kishore Kolli
Keyword(s):  
Object Detection ◽  
Detection Probability ◽  
Learning Approaches ◽  
Proposed Model ◽  
Detection Of Objects ◽  
Bounding Boxes ◽  
Probability Bounding ◽  
Range Of Values ◽  
Maximal Suppression ◽  
Better Than

Object detection is a stimulating task in the applications of computer vision. It is gaining a lot of attention in many real-time applications such as detection of number plates of suspect cars, identifying trespassers under surveillance areas, detecting unmasked faces in security gates during the COVID-19 period, etc. Region-based Convolution Neural Networks(R-CNN), You only Look once (YOLO) based CNNs, etc., comes under Deep Learning approaches. In this proposed work, an improved stacked Yolov3 model is designed for the detection of objects by bounding boxes. Hyperparameters are tuned to get optimum performance. The proposed model evaluated using the COCO dataset, and the performance is better than other existing object detection models. Anchor boxes are used for overlapping objects. After removing all the predicted bounding boxes that have a low detection probability, bounding boxes with the highest detection probability are selected and eliminated all the bounding boxes whose Intersection Over Union value is higher than 0.4. Non-Maximal Suppression (NMS) is used to only keep the best bounding box. In this experimentation, we have tried with various range of values, but finally got better result at threshold 0.5.

scholarly journals Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images

2020 ◽  
Vol 9 (6) ◽  
pp. 370
Author(s):  
Atakan Körez ◽  
Necaattin Barışçı ◽  
Aydın Çetin ◽  
Uçman Ergün
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Mean Average Precision ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Average Precision ◽  
Proposed Model ◽  
Detection Of Objects ◽  
Very High

The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).

scholarly journals Deep Transfer Learning Based Multiway Feature Pyramid Network for Object Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Parvinder Kaur ◽  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Class Prediction ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Detection Of Objects ◽  
Feature Pyramid ◽  
Bounding Boxes ◽  
Detection Quality ◽  
Base Network

Object detection is being widely used in many fields, and therefore, the demand for more accurate and fast methods for object detection is also increasing. In this paper, we propose a method for object detection in digital images that is more accurate and faster. The proposed model is based on Single-Stage Multibox Detector (SSD) architecture. This method creates many anchor boxes of various aspect ratios based on the backbone network and multiscale feature network and calculates the classes and balances of the anchor boxes to detect objects at various scales. Instead of the VGG16-based deep transfer learning model in SSD, we have used a more efficient base network, i.e., EfficientNet. Detection of objects of different sizes is still an inspiring task. We have used Multiway Feature Pyramid Network (MFPN) to solve this problem. The input to the base network is given to MFPN, and then, the fused features are given to bounding box prediction and class prediction networks. Softer-NMS is applied instead of NMS in SSD to reduce the number of bounding boxes gently. The proposed method is validated on MSCOCO 2017, PASCAL VOC 2007, and PASCAL VOC 2012 datasets and compared to existing state-of-the-art techniques. Our method shows better detection quality in terms of mean Average Precision (mAP).

scholarly journals RAPID TARGET DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES USING YOLO MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 1915-1920 ◽  
Author(s):  
Z. Wu ◽  
X. Chen ◽  
Y. Gao ◽  
Y. Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Object Detection ◽  
Remote Sensing Images ◽  
Regression Problem ◽  
Extraction Region ◽  
Proposed Model ◽  
Deep Convolution Neural Network ◽  
Bounding Boxes

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

scholarly journals Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5906
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Support Vector ◽  
Susceptibility Map ◽  
Human Society ◽  
Learning Approaches ◽  
Prevention Measures ◽  
Landslide Occurrence ◽  
Better Than

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

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.

An Unequal-Sized Unidirectional Loop Layout Design Problem Considering Empty Vehicle Trip

2010 ◽  
Vol 37-38 ◽  
pp. 116-121
Author(s):  
Yu Lan Li ◽  
Bo Li ◽  
Su Jun Luo
Keyword(s):  
Material Handling ◽  
Design Principle ◽  
Facility Layout ◽  
Original Model ◽  
Layout Problem ◽  
Proposed Model ◽  
Loop Layout ◽  
Material Handling Costs ◽  
Loop Layout Problem ◽  
Better Than

In the facility layout decisions, the previous general design principle is to minimize material handling costs, and the objective of these old models only considers the costs of loaded trip, without regard to empty vehicle trip costs, which do not meet the actual demand. In this paper, the unequal-sized unidirectional loop layout problem is analyzed, and the model of facility layout is improved. The objective of the new model is to minimize the total loaded and empty vehicle trip costs. To solve this model, a heuristic algorithm based on partheno-genetic algorithms is designed. Finally, an unequal-sized unidirectional loop layout problem including 12 devices is simulated. Comparison shows that the result obtained using the proposed model is 20.4% better than that obtained using the original model.

Spatio-Temporal Gaussianization Flows for Extreme Event Detection

2021 ◽  
Author(s):  
Miguel-Ángel Fernández-Torres ◽  
J. Emmanuel Johnson ◽  
María Piles ◽  
Gustau Camps-Valls
Keyword(s):  
Event Detection ◽  
Land Surface ◽  
Extreme Event ◽  
Learning Approaches ◽  
Proposed Model ◽  
Individual Contributions ◽  
Current Context ◽  
Spatio Temporal ◽  
The Individual ◽  
Anomaly Score

&lt;p&gt;Automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change. Machine learning approaches have excelled in detection of extremes and anomalies in Earth data cubes recently, but are typically both computationally costly and supervised, which hamper their wide adoption. We alternatively present here an unsupervised, efficient, generative approach for extreme event detection, whose performance is illustrated for drought detection in Europe during the severe Russian heat wave in 2010. The core architecture of the model is generic and could naturally be extended to the detection of other kinds of anomalies. First, it computes hierarchical appearance (spatial) and motion (temporal) representations of several informative Essential Climate Variables (ECVs), including soil moisture, land surface temperature, as well as features describing vegetation health. Then, these representations are combined using Gaussianization Flows that yield a spatio-temporal anomaly score. This allows the proposed model not only to detect droughts areas, but also to explain why they were produced, monitoring the individual contributions of each of the ECVs to the indicator at its output.&lt;/p&gt;

Fuzzy Heuristics for Sequential Linear Programming

1998 ◽  
Vol 120 (1) ◽  
pp. 17-23 ◽  
Author(s):  
E. L. Mulkay ◽  
S. S. Rao
Keyword(s):  
Fuzzy Logic ◽  
Linear Programming ◽  
Path Following ◽  
Human Observer ◽  
Sequential Linear Programming ◽  
Algorithm Performance ◽  
Primal Dual ◽  
Improve Algorithm ◽  
Range Of Values ◽  
Better Than

Numerical implementations of optimization algorithms often use parameters whose values are not strictly determined by the derivation of the algorithm, but must fall in some appropriate range of values. This work describes how fuzzy logic can be used to “control” such parameters to improve algorithm performance. This concept is shown with the use of sequential linear programming (SLP) due to its simplicity in implementation. The algorithm presented in this paper implements heuristics to improve the behavior of SLP based on current iterate values of design constraints and changes in search direction. Fuzzy logic is used to implement the heuristics in a form similar to what a human observer would do. An efficient algorithm, known as the infeasible primal-dual path-following interior-point method, is used for solving the sequence of LP problems. Four numerical examples are presented to show that the proposed SLP algorithm consistently performs better than the standard SLP algorithm.

scholarly journals The coalescent process in models with selection, recombination and geographic subdivision

1991 ◽  
Vol 57 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Norman Kaplan ◽  
Richard R. Hudson ◽  
Masaru Iizuka
Keyword(s):  
Genetic Variation ◽  
Population Genetic ◽  
Genetic Model ◽  
Sequence Data ◽  
Balancing Selection ◽  
Similar Model ◽  
Proposed Model ◽  
Coalescent Approach ◽  
Neutral Mutations ◽  
Better Than

SummaryA population genetic model with a single locus at which balancing selection acts and many linked loci at which neutral mutations can occur is analysed using the coalescent approach. The model incorporates geographic subdivision with migration, as well as mutation, recombination, and genetic drift of neutral variation. It is found that geographic subdivision can affect genetic variation even with high rates of migration, providing that selection is strong enough to maintain different allele frequencies at the selected locus. Published sequence data from the alcohol dehydrogenase locus of Drosophila melanogaster are found to fit the proposed model slightly better than a similar model without subdivision.

scholarly journals Both Freud and Hoffman are Right: Anxious-Aggressive and Empathic Dimensions of Guilt

2008 ◽  
Vol 11 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Itziar Etxebarria ◽  
Pedro Apodaca
Keyword(s):  
Young Adults ◽  
Subjective Experience ◽  
Negative Emotions ◽  
The Other ◽  
Point Scale ◽  
Nested Models ◽  
Proposed Model ◽  
Better Than

The purpose of the study was to confirm a model which proposed two basic dimensions in the subjective experience of guilt, one anxious-aggressive and the other empathic, as well as another dimension associated but not intrinsic to it, namely, the associated negative emotions dimension. Participants were 360 adolescents, young adults and adults of both sexes. They were asked to relate one of the situations that most frequently caused them to experience feelings of guilt and to specify its intensity and that of 9 other emotions that they may have experienced, to a greater or lesser extent, at the same time on a 7-point scale. The proposed model was shown to adequately fit the data and to be better than other alternative nested models. This result supports the views of both Freud and Hoffman regarding the nature of guilt, contradictory only at a first glance.

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