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The focus of this work is on energy-efficient computing for deep learning-based thermal bridge (anomaly) detection models. In this study, we concentrate on object detection-based models such as Mask R-CNN_FPN_50, Swin-T Transformer, and FSAF. We do benchmark tests on TBRR dataset with varying input sizes. To overcome the energy-efficient design, we apply optimizations such as compression, latency reduction, and pruning to these models. After our proposed improvements, the inference of the anomaly detection model, Mask R-CNN_FPN_50 with compression technique, is approximately 7.5% faster than the original. Also, more acceleration is observed in all models with increasing input size. Another criterion we focus on is total energy gain for optimized models. Swin-T transformer has the most inference energy gains for all input sizes (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\approx$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u2248<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>27 J for 3000 x 4000 and <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\approx$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u2248<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>14 J for 2400 x 3400). In conclusion, our study presents an optimization of size, weight, and power for vision-based anomaly detection for buildings.<\/jats:p>","DOI":"10.1007\/s10586-024-04624-y","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T19:02:15Z","timestamp":1718650935000},"page":"12787-12797","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection"],"prefix":"10.1007","volume":"27","author":[{"given":"Alparslan","family":"Fi\u015fne","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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