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Full text: BAnA Studie zur Bewertung von Algorithmen für nautische Anwendungen

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[48] Carion, Nicolas, Massa, Francisco, Synnaeve, Gabriel, Usunier, Nicolas, Kirillov, Alexander und Zagoruyko, Ser- 
gey: End-to-End Object Detection with Transformers. European conference on computer vision (pp. 213-229). 
BPEngerInfernational Publishing., abs/2005.12872, 2020. https ://api.semanticscholar.org/CorpusID:. 
18889833. 
[49] Yang, Jianwei, Li, Chunyuan, Zhang, Pengchuan, Dai, Xiyang, Xiao, Bin, Yuan, Lu und Gao, Jianfeng: Focal 
Attention_for_Long-Range Interactions_in_ Vision_Transformers. _ In: Neural Information Processing Systems, 
2021. https z 7er .semanticscholar. org/CorpusID z 2450111448. 
[50] Wang, Chien Yao, Bochkovskiy, Alexey und Liao, Hong Yuan Mark: YOLOVv7: Trainable Bag-of-Freebies Sets 
New State-of-the-Art for Real-Time Object Detectors. 2023 I|EEE/CVF Conference on Computer Vision_and 
Pattern_Recognition (CVPR), Seiten 7464-7475, 2022. https://api.semanticscholar.org/CorpusID a 
25031 1204. 
[51] Lin, Tsung Yi, Maire, Michael, Belongie, Serge J., Hays, James, Perona, Pietro, Ramanan, Deva, Dollär, Piotr und 
Zitnick, C. Lawrence: Microsoft COCO: Common Objects in Context. In: European Conference on Computer 
Vision, 2014. https ://api.semanticscholar.org/CorpusID: 14113767. 
[52] Chen, Liang Chieh, Papandreou, George, Schroff, Florian und Adam, Hartwig: Rethinking Atrous Convolution 
for_Semantic Image Segmentation. ArXiv, abs/1706.05587, 2017. https://api.semanticscholar org. 
CorpusID:2265519. 
[53] Howard, Andrew G., Sandler, Mark, Chu, Grace, Chen, Liang Chieh, Chen, Bo, Tan, Mingxing, Wang, Weijun, 
Zhu, Yukun, Pang, Ruoming, Vasudevan, Vijay, Le, Quoc V. und Adam, Hartwig: Searching for MobileNetV3. 
2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seiten 1314-1324, 2019. https: /A 
api.semanticscholar .org/CorpusID } 14680833. 
[54] Yuan, Yuhui, Chen, Xilin und Wang, Jingdong: Object-Contextual Representations for Semantic Segqgmentati- 
on. In: European Conference on Computer Vision, 2019. https ://api.semanticscholar.org/CorpusID: 
bo273436d. 
[55] Xie, Enze, Wang, Wenhai, Yu, Zhiding, Anandkumar, Anima, Alvarez, Jose M und Luo, Ping: SegFormer: Simple 
and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing 
Systems, 34:12077-12090, 2021. 
[56] Chen, Zhe, Duan, Yuchen, Wang, Wenhai, He, Junjun, Lu, Tong, Dai, Jifeng und Qiao, Y.: Vision Transformer 
Adapter for Dense Predictions. The Eleventh International Conference on Learning Representations, ICLR 2023, 
Kigali, Rwanda, May 1-5, 2023, abs/2205.08534, 2023. h ttps://api.semanticscholar.org/CorpusID: 
248834104. 
[57] Zhou, Bolei, Zhao, Hang, Puig, Xavier, Fidler, Sanja, Barriuso, Adela und Torralba, Antonio: Scene Parsing 
through ADE2O0K Dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 
Seiten 5122-5130, 2017. 
[58] Cordts, Marius, Omran, Mohamed, Ramos, Sebastian, Rehfeld, Timo, Enzweiler, Markus, Benenson, Rodrigo, 
-ranke, Uwe, Roth, Stefan und Schiele, Bernt: The Cityscapes Dataset for Semantic Urban Scene Understan- 
ding. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 
[59] Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, 
Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael S., Berg, Alexander C. und Fei-Fei, Li: /mageNet Lar- 
96 Scale_Visual_Recognition_Challenge. _ International Journal of Computer Vision, 115:211 — 252, 2014. 
ttps://api.semanticscholar.org/CorpusID: 293054 7. 
[60] Prasad, Dilip K., Rajan, Deepu, Rachmawati, Lily, Rajabally, Eshan und Quek, Chai: Video Processing From 
Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey. |EEE _Tran- 
sactions on Intelligent Transportation Systems, 18:1993-2016, 2016. https ://api.semanticscholar.org/ 
ZJorpusID:11905373. 
[61] lancu, Bogdan, Soloviev, Valentin, Zelioli, Luca und Lilius, Johan: ABOShips - An Inshore and_Offshore_Ma- 
ritime_Vessel_Detection_Dataset_with_Precise Annotations. ArXiv, abs/2102.05869, 2021. https: //api.| 
semanticscholar.org/CorpusID: 231879723. 
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