{"doi":"10.1109/synasc69064.2025.00034","title":"Optimized DeepLabV3+ Architecture for Semantic Segmentation","abstract":"Designing highly accurate lightweight semantic segmentation models is an essential task in the computer vision field due to the necessity to deploy these segmentation models in real-world scenarios. Developing optimized architectures is a challenging research direction since it needs to maintain a right balance between segmentation accuracy and inference speed. Therefore, we consider to rethink an advanced semantic segmentation model, called DeepLabV3+, into a more lightweight approach, enhancing its real-time performance. Through our method, we achieve better optimization across three important performance metrics, i.e., inference speed, GFLOPs (Giga Floating Point Operations), and the number of learnable parameters, while maintaining the accuracy at the same level. Our method is validated through extensive experiments conducted on two urban street scene datasets, namely Cityscapes and CamVid. In particular, on the Cityscapes dataset, our optimized model demonstrates a significant speed-up improvement from 276 FPS to 289 FPS on <tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$512 \\times 1024$</tex> input resolution, a 75% reduction in learnable parameters, and a lower computational load of the model, 10.8 GFLOPs compared to 18.8 GFLOPs, and all these results occur while preserving the 74% accuracy level.","journal":null,"year":2025,"id":588016,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9538,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1504151,"name":"Călin-Adrian Popa","orcid":"0000-0003-4445-8091","position":1,"is_corresponding":false},{"id":1504351,"name":"Cristina Cărunta","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T02:59:43.096742Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}