AI SearchAdaptive lightweight license plate image recovery using deep learning based on generative adversarial network (Record no. 1610)
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| fixed length control field | 03940nas a2200421 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260817161526.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260112s20249999th u ms t 000 eng d |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12469816 |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT Diss. no.ISE-24-02 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Wuttinan Sereethavekul |
| 245 10 - TITLE STATEMENT | |
| Title | Adaptive lightweight license plate image recovery using deep learning based on generative adversarial network |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 109 leaves : |
| Other physical details | ill. +1 online resource |
| 490 1# - SERIES STATEMENT | |
| Series statement | Dissertation ; |
| Volume/sequential designation | no. ISE-24-02 |
| 500 ## - GENERAL NOTE | |
| General note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Microelectronics and Embedded Systems |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (Ph. D.) - Asian Institute of Technology, 2024 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Many Convolutional Neural Networks (CNNs) methods have already surpassed tradi tional approaches to image restoration tasks. Those CNNs models were usually de signed to enhance single tasks such as an image resolution (super-resolution) or image denoising, but we came up with unconventional goals, that is, multiple recovery tasks from a single network design. Although the Transformer design has recently gained at tention in image recovery tasks, they are too slow. In order to work with license plate images from a traffic camera stream, the system has to be responsive. So, we proposed a fast and lightweight deep learning-based data recovery system using a Generative Ad versarial Network (GAN) principle named License Plate Recovery GAN (LPRGAN). The design has a proposed encoder-decoder style inspired by an autoencoder aided by dual classification networks. This style suits problem-characteristic learning because strong contextual information is retrieved from the down-scaled representations. This proposed system has three main features such as identifying a problem, data recovery, and fail-safe mechanism. The core of system is a data recovery unit (LPRGAN), is used to recover license plate images from multiple degraded input images. Most existing im age restoration systems do not have self-awareness, leading to an inefficiency problem. Unlike existing works, this system has anomaly detection and will only process on a de graded input, reducing workload overhead, improving efficiency and a fail-safe feature that prevents an unexpected bad output. Hence, the proposed algorithm requires less resource to deploy on a low-power machine such as edge computing devices, opening up newpossibilities in on-device computing. Our proposed research can recover several degraded problems up to 720p resolution at 15 frames per second on a single graphic card, 256x128 resolution at 17 frames per second on a CPU-only workstation machine, or 7 frames per second on an ultra-low-power tablet PC. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine Learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data recovery (Computer science) |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mongkol Ekpanyapong, |
| Relator term | Chairperson |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dailey, Matthew N., |
| Relator term | Examination Committee |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huynh, Trung Luong, |
| Relator term | Examination Committee |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Royal Thai Government, |
| Relator term | Scholarship Donor |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | AIT Fellowship, |
| Relator term | Scholarship Donor |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Dissertation ; |
| Volume/sequential designation | no. ISE-24-02 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B23315">http://203.159.5.9/ait-thesis/detail.php?q=B23315</a> |
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| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| Operator's initials, OID (RLIN) | 0 |
| Cataloger's initials, CIN (RLIN) | 260116 |
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| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 40-Archives |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 61-CD-ROM |
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| Barcode | Barcode : 30050120422661 |
| CREATED | CREATED : 2026-12-01 |
| RECORD Id | RECORD # : i13565473 |
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| CREATED | CREATED : 2026-12-01 |
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| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 17/08/2026 | AIT Diss. no.ISE-24-02 | 30050120422661 | 17/08/2026 | 1 | 17/08/2026 | 40-Archives | ||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 17/08/2026 | AIT Diss. no.ISE-24-02 | 17/08/2026 | 1 | 17/08/2026 | 61-CD-ROM |