scholarly journals Multiobject Detection Algorithm Based on Adaptive Default Box Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinling Li ◽  
Qingshan Hou ◽  
Jinsheng Xing
Keyword(s):  
Semantic Information ◽  
Detection Algorithm ◽  
Single Shot ◽  
Small Object ◽  
Important Research ◽  
Important Research Topic ◽  
Complex Scenes ◽  
Small Object Detection ◽  
Detection Effect ◽  
Improved Algorithm

Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

scholarly journals Water surface object detection using panoramic vision based on improved single-shot multibox detector

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Aofeng Li ◽  
Xufang Zhu ◽  
Shuo He ◽  
Jiawei Xia
Keyword(s):  
Object Detection ◽  
Water Surface ◽  
Detection Algorithm ◽  
Single Shot ◽  
Panoramic Vision ◽  
Feature Pyramid ◽  
The Mean ◽  
Detection Effect ◽  
Surface Object ◽  
Improved Algorithm

AbstractIn view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.

scholarly journals Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Haotian Li ◽  
Kezheng Lin ◽  
Jingxuan Bai ◽  
Ao Li ◽  
Jiali Yu
Keyword(s):  
Object Detection ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Scale Invariant ◽  
Feature Pyramid ◽  
Small Object Detection ◽  
Detection And Localization

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed. Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel. Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers. Finally, the obtained layer is used for detection and localization. The final location coordinates and confidence are output after nonmaximum suppression. Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.

scholarly journals SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3630 ◽  
Author(s):  
Young-Joon Hwang ◽  
Jin-Gu Lee ◽  
Un-Chul Moon ◽  
Ho-Hyun Park
Keyword(s):  
Object Detection ◽  
Semantic Information ◽  
Feature Fusion ◽  
Contextual Information ◽  
Single Shot ◽  
Small Object ◽  
Dilated Convolution ◽  
Average Improvement ◽  
Proposed Model ◽  
Small Object Detection

The single shot multi-box detector (SSD) exhibits low accuracy in small-object detection; this is because it does not consider the scale contextual information between its layers, and the shallow layers lack adequate semantic information. To improve the accuracy of the original SSD, this paper proposes a new single shot multi-box detector using trident feature and squeeze and extraction feature fusion (SSD-TSEFFM); this detector employs the trident network and the squeeze and excitation feature fusion module. Furthermore, a trident feature module (TFM) is developed, inspired by the trident network, to consider the scale contextual information. The use of this module makes the proposed model robust to scale changes owing to the application of dilated convolution. Further, the squeeze and excitation block feature fusion module (SEFFM) is used to provide more semantic information to the model. The SSD-TSEFFM is compared with the faster regions with convolution neural network features (RCNN) (2015), SSD (2016), and DF-SSD (2020) on the PASCAL VOC 2007 and 2012 datasets. The experimental results demonstrate the high accuracy of the proposed model in small-object detection, in addition to a good overall accuracy. The SSD-TSEFFM achieved 80.4% mAP and 80.2% mAP on the 2007 and 2012 datasets, respectively. This indicates an average improvement of approximately 2% over other models.

scholarly journals Vehicle and Vessel Detection on Satellite Imagery: A Comparative Study on Single-Shot Detectors

2020 ◽  
Vol 12 (7) ◽  
pp. 1217 ◽  
Author(s):  
Tanguy Ophoff ◽  
Steven Puttemans ◽  
Vasileios Kalogirou ◽  
Jean-Philippe Robin ◽  
Toon Goedemé
Keyword(s):  
Object Detection ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Single Shot ◽  
Small Object ◽  
Average Precision ◽  
Vessel Detection ◽  
Speed Up ◽  
Small Object Detection ◽  
The Many

In this paper, we investigate the feasibility of automatic small object detection, such as vehicles and vessels, in satellite imagery with a spatial resolution between 0.3 and 0.5 m. The main challenges of this task are the small objects, as well as the spread in object sizes, with objects ranging from 5 to a few hundred pixels in length. We first annotated 1500 km2, making sure to have equal amounts of land and water data. On top of this dataset we trained and evaluated four different single-shot object detection networks: YOLOV2, YOLOV3, D-YOLO and YOLT, adjusting the many hyperparameters to achieve maximal accuracy. We performed various experiments to better understand the performance and differences between the models. The best performing model, D-YOLO, reached an average precision of 60% for vehicles and 66% for vessels and can process an image of around 1 Gpx in 14 s. We conclude that these models, if properly tuned, can thus indeed be used to help speed up the workflows of satellite data analysts and to create even bigger datasets, making it possible to train even better models in the future.

A lightweight small object detection algorithm based on improved SSD

2018 ◽  
Vol 47 (7) ◽  
pp. 703005 ◽  
Author(s):  
吴天舒 Wu Tianshu ◽  
张志佳 Zhang Zhijia ◽  
刘云鹏 Liu Yunpeng ◽  
裴文慧 Pei Wenhui ◽  
陈红叶 Chen Hongye
Keyword(s):  
Object Detection ◽  
Detection Algorithm ◽  
Small Object ◽  
Small Object Detection

scholarly journals FASSD: A Feature Fusion and Spatial Attention-Based Single Shot Detector for Small Object Detection

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1536
Author(s):  
Deng Jiang ◽  
Bei Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
Peng Wu ◽  
...  
Keyword(s):  
Object Detection ◽  
Spatial Attention ◽  
Feature Fusion ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Feature Representations ◽  
Small Object Detection ◽  
High Level ◽  
Detection Speed

Deep learning methods have significantly improved object detection performance, but small object detection remains an extremely difficult and challenging task in computer vision. We propose a feature fusion and spatial attention-based single shot detector (FASSD) for small object detection. We fuse high-level semantic information into shallow layers to generate discriminative feature representations for small objects. To adaptively enhance the expression of small object areas and suppress the feature response of background regions, the spatial attention block learns a self-attention mask to enhance the original feature maps. We also establish a small object dataset (LAKE-BOAT) of a scene with a boat on a lake and tested our algorithm to evaluate its performance. The results show that our FASSD achieves 79.3% mAP (mean average precision) on the PASCAL VOC2007 test with input 300 × 300, which outperforms the original single shot multibox detector (SSD) by 1.6 points, as well as most improved algorithms based on SSD. The corresponding detection speed was 45.3 FPS (frame per second) on the VOC2007 test using a single NVIDIA TITAN RTX GPU. The test results of a simplified FASSD on the LAKE-BOAT dataset indicate that our model achieved an improvement of 3.5% mAP on the baseline network while maintaining a real-time detection speed (64.4 FPS).

scholarly journals Supernovae Detection with Fully Convolutional One-Stage Framework

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1926
Author(s):  
Kai Yin ◽  
Juncheng Jia ◽  
Xing Gao ◽  
Tianrui Sun ◽  
Zhengyin Zhou
Keyword(s):  
Object Detection ◽  
Data Augmentation ◽  
Performance Enhancement ◽  
Detection Algorithm ◽  
Small Object ◽  
Learning Methods ◽  
Detection Algorithms ◽  
One Stage ◽  
Art Object ◽  
Small Object Detection

A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such data have huge quantity and sparse positives. While the traditional machine learning methods can be used to deal with such data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful adaptability in this area. However, most data in the existing works are either simulated or without generality. How do the state-of-the-art object detection algorithms work on real supernova data is largely unknown, which greatly hinders the development of this field. Furthermore, the existing works of supernovae classification usually assume the input images are properly cropped with a single candidate located in the center, which is not true for our dataset. Besides, the performance of existing detection algorithms can still be improved for the supernovae detection task. To address these problems, we collected and organized all the known objectives of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in two datasets, and then compared several detection algorithms on them. After that, the selected Fully Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data augmentation, attention mechanism, and small object detection technique. Extensive experiments demonstrate the great performance enhancement of our detection algorithm with the new datasets.

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