Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

He, Pan; Wu, Aotian; Huang, Xiaohui; Rangarajan, Anand; Ranka, Sanjay

doi:10.3390/app12042075

Open AccessArticle

Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

by

Pan He

^†

,

Aotian Wu

^†

,

Xiaohui Huang

,

Anand Rangarajan

and

Sanjay Ranka

^*

Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Appl. Sci. 2022, 12(4), 2075; https://doi.org/10.3390/app12042075

Submission received: 7 January 2022 / Revised: 4 February 2022 / Accepted: 6 February 2022 / Published: 16 February 2022

(This article belongs to the Collection Machine Learning in Computer Engineering Applications)

Download

Browse Figures

Versions Notes

Abstract

In this paper, we propose a novel approach to commodity classification from surveillance videos by utilizing logo data on trucks. Broadly, most logos can be classified as predominantly text or predominantly images. For the former, we leverage state-of-the-art deep-learning-based text recognition algorithms on images. For the latter, we develop a two-stage image retrieval algorithm consisting of a universal logo detection stage that outputs all potential logo positions, followed by a logo recognition stage designed to incorporate advanced image representations. We develop an integrated approach to combine predictions from both the text-based and image-based solutions, which can help determine the commodity type that is potentially being hauled by trucks. We evaluated these models on videos collected in collaboration with the state transportation entity and achieved promising performance. This, along with prior work on trailer classification, can be effectively used for automatically deriving commodity types for trucks moving on highways.

Keywords: freight analysis; scene text understanding; logo detection and recognition; commodity classification; deep learning; intelligent transportation system

Share and Cite

MDPI and ACS Style

He, P.; Wu, A.; Huang, X.; Rangarajan, A.; Ranka, S. Machine Learning-Based Highway Truck Commodity Classification Using Logo Data. Appl. Sci. 2022, 12, 2075. https://doi.org/10.3390/app12042075

AMA Style

He P, Wu A, Huang X, Rangarajan A, Ranka S. Machine Learning-Based Highway Truck Commodity Classification Using Logo Data. Applied Sciences. 2022; 12(4):2075. https://doi.org/10.3390/app12042075

Chicago/Turabian Style

He, Pan, Aotian Wu, Xiaohui Huang, Anand Rangarajan, and Sanjay Ranka. 2022. "Machine Learning-Based Highway Truck Commodity Classification Using Logo Data" Applied Sciences 12, no. 4: 2075. https://doi.org/10.3390/app12042075

APA Style

He, P., Wu, A., Huang, X., Rangarajan, A., & Ranka, S. (2022). Machine Learning-Based Highway Truck Commodity Classification Using Logo Data. Applied Sciences, 12(4), 2075. https://doi.org/10.3390/app12042075

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI