Unsupervised hybrid anomaly detection model for logistics fleet management systems

Phiboonbanakit, Thananut; Huynh, Van–Nam; Horanont, Teerayut; Supnithi, Thepchai

doi:10.1049/iet-its.2019.0167

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Detection technologies such as road detection, pedestrian detection, and vehicle detection are fundamental to self-driving. An accurate road detection is the core of vehicle navigation and also the basis for the car to complete other tasks such as anomaly detection [1] and lane detection [2,3], so it is vital to detect the road specifically.…”

Section: Introductionmentioning

confidence: 99%

A two‐scaled fully convolutional learning network for road detection

Liang

2021

IET Image Processing

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This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.

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Section: Introductionmentioning

confidence: 99%