Mobile-based hazmat sign detection and recognition

Zhao, Bin; Parra, Albert; Delp, Edward J.

doi:10.1109/globalsip.2013.6736996

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those methods, however, can be affected by perspective distortion from different viewpoints. Saliency-based methods use saliency detection to speed up the detection process and get the region of interest where the hazmat sign might locate in [15] [16]. Recent research focued on CNN methods, such as [17] [18].…”

Section: A Hazmat Sign Detectionmentioning

confidence: 99%

Improving CNN-based Planar Object Detection with Geometric Prior Knowledge

Cai,

Hou,

et al. 2019

Preprint

View full text Add to dashboard Cite

In this paper, we focus on the question: how might mobile robots take advantage of affordable RGB-D sensors for object detection? Although current CNN-based object detectors have achieved impressive results, there are three main drawbacks for practical usage on mobile robots: 1) It is hard and time-consuming to collect and annotate large-scale training sets.2) It usually needs a long training time. 3) CNN-based object detection shows significant weakness in predicting location. We propose a novel approach for the detection of planar objects, which rectifies images with geometric information to compensate for the perspective distortion before feeding it to the CNN detector module, typically a CNN-based detector like YOLO or MASK RCNN. By dealing with the perspective distortion in advance, we eliminate the need for the CNN detector to learn that. Experiments show that this approach significantly boosts the detection performance. Besides, it effectively reduces the number of training images required. In addition to the novel detection framework proposed, we also release an RGB-D dataset for hazmat sign detection. To the best of our knowledge, this is the first public-available hazmat sign detection dataset with RGB-D sensors.

show abstract

Section: A Hazmat Sign Detectionmentioning

confidence: 99%