{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:52:44Z","timestamp":1776091964371,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for mmWave Smart Radar Systems and Technologies","award":["MOST 110-2221-E-A49-145-MY3"],"award-info":[{"award-number":["MOST 110-2221-E-A49-145-MY3"]}]},{"name":"Center for mmWave Smart Radar Systems and Technologies","award":["MOST 111-2634-F-A49-009"],"award-info":[{"award-number":["MOST 111-2634-F-A49-009"]}]},{"name":"Center for mmWave Smart Radar Systems and Technologies","award":["111-2218-E-A49-028-"],"award-info":[{"award-number":["111-2218-E-A49-028-"]}]},{"name":"Center for mmWave Smart Radar Systems and Technologies","award":["MOST 110-2634-F-A49-004"],"award-info":[{"award-number":["MOST 110-2634-F-A49-004"]}]},{"name":"Ministry of Education (MOE)","award":["MOST 110-2221-E-A49-145-MY3"],"award-info":[{"award-number":["MOST 110-2221-E-A49-145-MY3"]}]},{"name":"Ministry of Education (MOE)","award":["MOST 111-2634-F-A49-009"],"award-info":[{"award-number":["MOST 111-2634-F-A49-009"]}]},{"name":"Ministry of Education (MOE)","award":["111-2218-E-A49-028-"],"award-info":[{"award-number":["111-2218-E-A49-028-"]}]},{"name":"Ministry of Education (MOE)","award":["MOST 110-2634-F-A49-004"],"award-info":[{"award-number":["MOST 110-2634-F-A49-004"]}]},{"name":"Pervasive Artificial Intelligence Research Labs (PAIR Labs) in Taiwan","award":["MOST 110-2221-E-A49-145-MY3"],"award-info":[{"award-number":["MOST 110-2221-E-A49-145-MY3"]}]},{"name":"Pervasive Artificial Intelligence Research Labs (PAIR Labs) in Taiwan","award":["MOST 111-2634-F-A49-009"],"award-info":[{"award-number":["MOST 111-2634-F-A49-009"]}]},{"name":"Pervasive Artificial Intelligence Research Labs (PAIR Labs) in Taiwan","award":["111-2218-E-A49-028-"],"award-info":[{"award-number":["111-2218-E-A49-028-"]}]},{"name":"Pervasive Artificial Intelligence Research Labs (PAIR Labs) in Taiwan","award":["MOST 110-2634-F-A49-004"],"award-info":[{"award-number":["MOST 110-2634-F-A49-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a deep learning based object detection method to locate a distant region in an image in real-time. It concentrates on distant objects from a vehicular front camcorder perspective, trying to solve one of the common problems in Advanced Driver Assistance Systems (ADAS) applications, which is, to detect the smaller and faraway objects with the same confidence as those with the bigger and closer objects. This paper presents an efficient multi-scale object detection network, termed as ConcentrateNet to detect a vanishing point and concentrate on the near-distant region. Initially, the object detection model inferencing will produce a larger scale of receptive field detection results and predict a potentially vanishing point location, that is, the farthest location in the frame. Then, the image is cropped near the vanishing point location and processed with the object detection model for second inferencing to obtain distant object detection results. Finally, the two-inferencing results are merged with a specific Non-Maximum Suppression (NMS) method. The proposed network architecture can be employed in most of the object detection models as the proposed model is implemented in some of the state-of-the-art object detection models to check feasibility. Compared with original models using higher resolution input size, ConcentrateNet architecture models use lower resolution input size, with less model complexity, achieving significant precision and recall improvements. Moreover, the proposed ConcentrateNet architecture model is successfully ported onto a low-powered embedded system, NVIDIA Jetson AGX Xavier, suiting the real-time autonomous machines.<\/jats:p>","DOI":"10.3390\/s22197371","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T01:23:16Z","timestamp":1664414596000},"page":"7371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique"],"prefix":"10.3390","volume":"22","author":[{"given":"Bo-Xun","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Institute of Electronics, National Yang Ming Chiao Tung University (NYCU), Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9434-5899","authenticated-orcid":false,"given":"Vinay M.","family":"Shivanna","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Institute of Electronics, National Yang Ming Chiao Tung University (NYCU), Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsiang-Hsuan","family":"Hung","sequence":"additional","affiliation":[{"name":"Chunghua Telecom Co., Ltd., Taoyuan 32661, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0402-2621","authenticated-orcid":false,"given":"Jiun-In","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Institute of Electronics, National Yang Ming Chiao Tung University (NYCU), Hsinchu 30010, Taiwan"},{"name":"Wistron-NCTU Embedded Artificial Intelligence Research Center, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"},{"name":"Pervasive Artificial Intelligence Research (PAIR) Labs, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singh, B., and Davis, L.S. 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