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Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.<\/jats:p>","DOI":"10.3390\/s19163523","type":"journal-article","created":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T06:38:02Z","timestamp":1565591882000},"page":"3523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Real-Time Water Surface Object Detection Based on Improved Faster R-CNN"],"prefix":"10.3390","volume":"19","author":[{"given":"Lili","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Jie","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Huibin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Hohai University, Nanjing 211100, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1016\/j.procs.2016.05.455","article-title":"GPU-based pedestrian detection for autonomous driving","volume":"80","author":"Campmany","year":"2016","journal-title":"Procedia Comput. 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The system employs an eye-in-hand 2D camera on a UR5 robotic arm to collect real-time RGB images, which are processed through the dual-model architecture to detect and classify objects from 36 categories. Training and validation were conducted using a publicly available fruit and vegetable dataset to simulate an industrial sorting application. The combined classification accuracy reaches 83%, with high F1-scores for most classes. This architecture provides visual recognition capabilities and real-time processing suitable for automated industrial settings.<\/p>","DOI":"10.21203\/rs.3.rs-8651114\/v1","type":"posted-content","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T19:34:56Z","timestamp":1771270496000},"source":"Crossref","is-referenced-by-count":0,"title":["Vision-Based Pick and Place Robots Using Faster R-CNN and EfficientNet for Real-Time Object Detection and Classification"],"prefix":"10.21203","author":[{"given":"Santhoshkumar","family":"Sivakumar","sequence":"first","affiliation":[{"name":"PSG College of Technology"}]},{"given":"Jayanth Subramaniam","family":"A","sequence":"additional","affiliation":[{"name":"PSG College of Technology"}]},{"given":"Senthil Kumar","family":"K","sequence":"additional","affiliation":[{"name":"PSG College of 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