289 research outputs found
Detection of Sand Boils from Images using Machine Learning Approaches
Levees provide protection for vast amounts of commercial and residential properties. However, these structures degrade over time, due to the impact of severe weather, sand boils, subsidence of land, seepage, etc. In this research, we focus on detecting sand boils. Sand boils occur when water under pressure wells up to the surface through a bed of sand. These make levees especially vulnerable. Object detection is a good approach to confirm the presence of sand boils from satellite or drone imagery, which can be utilized to assist in the automated levee monitoring methodology. Since sand boils have distinct features, applying object detection algorithms to it can result in accurate detection. To the best of our knowledge, this research work is the first approach to detect sand boils from images. In this research, we compare some of the latest deep learning methods, Viola Jones algorithm, and other non-deep learning methods to determine the best performing one. We also train a Stacking-based machine learning method for the accurate prediction of sand boils. The accuracy of our robust model is 95.4%
Precise Single-stage Detector
There are still two problems in SDD causing some inaccurate results: (1) In
the process of feature extraction, with the layer-by-layer acquisition of
semantic information, local information is gradually lost, resulting into less
representative feature maps; (2) During the Non-Maximum Suppression (NMS)
algorithm due to inconsistency in classification and regression tasks, the
classification confidence and predicted detection position cannot accurately
indicate the position of the prediction boxes. Methods: In order to address
these aforementioned issues, we propose a new architecture, a modified version
of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector
(PSSD). Firstly, we improve the features by adding extra layers to SSD.
Secondly, we construct a simple and effective feature enhancement module to
expand the receptive field step by step for each layer and enhance its local
and semantic information. Finally, we design a more efficient loss function to
predict the IOU between the prediction boxes and ground truth boxes, and the
threshold IOU guides classification training and attenuates the scores, which
are used by the NMS algorithm. Main Results: Benefiting from the above
optimization, the proposed model PSSD achieves exciting performance in
real-time. Specifically, with the hardware of Titan Xp and the input size of
320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28
mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object
detection models. Besides, the proposed model performs significantly well with
larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS
COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove
that the proposed model has a better trade-off between speed and accuracy.Comment: We will submit it soon to the IEEE transaction. Due to characters
limitation, we can not upload the full abstract. Please read the pdf file for
more detai
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware
- …