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| Title: | 應用生成式資料擴增提升魚眼鏡頭物件偵測模型效能
Enhancing Fisheye Lens Object Detection Using Generative Data Augmentation |
| Authors: | 程品潔
Cheng, Pin-Chieh |
| Contributors: | 廖文宏
Liao, Wen-Hung
程品潔
Cheng, Pin-Chieh |
| Keywords: | 魚眼鏡頭
魚眼校正
物件偵測
擴散模型
生成式資料擴增
Fisheye Camera
Fisheye Correction
Object Detection
Diffusion Model
Generative Data Augmentation |
| Date: | 2024 |
| Issue Date: | 2024-08-05 13:56:22 (UTC+8) |
| Abstract: | 智慧城市旨在利用創新科技提升城市運行效率、安全性及生活品質，先進的監控系統和物件偵測技術是智慧城市的重要組成部分，有助於管理和優化公共空間。頂照式魚眼鏡頭因其超廣角視野，非常適合用於大範圍監控，但也帶來了嚴重的影像失真問題。此外，鑒於隱私保護需求和場景的多樣性，獲取足夠且多樣化的公開影像極其困難，從而阻礙了相關研究的發展。
針對上述問題，本研究選擇圖書館這一常見且重要的公共場所作為研究對象，旨在解決使用頂照式魚眼鏡頭進行物件偵測時的兩大挑戰：資料稀缺性和魚眼影像失真問題。透過使用文本到圖像的生成模型來擴增訓練資料，並結合基於預設相機內部參數的失真校正方法，我們成功提高了物件偵測的準確率。
實驗結果顯示，使用生成式AI模型生成的圖像進行訓練，並逐步策略性地增加合成實例，能夠顯著提升模型的偵測性能，將資料集校正後對小尺寸物件的偵測效果尤其顯著。我們提出的魚眼校正微調模型跟YOLOv8基準模型相比，整體的mAP(0.5) 從0.246提升到0.688，mAP(0.5-0.95) 從0.122提升到0.518；在特定的小尺寸物件類別（飲料）上，mAP(0.5) 從0.507提升到0.795，mAP(0.5-0.95) 從0.268提升到0.586。此外，混合適當比例的合成資料與真實資料進行訓練，不僅可以提升訓練過程的穩健性，還有助於進一步優化模型性能。這些發現證實了本研究採用的方法在頂照式魚眼鏡頭物件偵測應用中的潛力。
Smart cities aim to leverage innovative technologies to enhance urban operational efficiency, safety, and quality of life. Advanced surveillance systems and object detection technologies are crucial components of smart cities, aiding in the management and optimization of public spaces. Overhead fisheye lenses, with their ultra-wide field of view, are well-suited for large-scale surveillance but present significant image distortion challenges. Furthermore, due to privacy protection requirements and the diversity of scenes, acquiring sufficient and diverse public images is extremely difficult, hindering the development of related research.
Addressing these issues, this study focuses on libraries, a common and important public venue, aiming to address two major challenges in object detection using overhead fisheye images: data scarcity and fisheye lens distortion. By using text-to-image generative models to augment the training data and combining them with distortion correction methods based on preset camera intrinsic parameters, we successfully improved object detection accuracy.
Experimental results show that training with images generated by generative AI models and gradually and strategically increasing synthetic instances can significantly enhance the model's detection performance, with particularly notable improvements in detecting small objects after correcting the dataset. Our fisheye correction fine-tuning model, compared to the YOLOv8 baseline model, improved the overall mAP(0.5) from 0.246 to 0.688 and mAP(0.5-0.95) from 0.122 to 0.518; for specific small object categories (beverages), mAP(0.5) increased from 0.507 to 0.795, and mAP(0.5-0.95) from 0.268 to 0.586. Additionally, mixing an appropriate proportion of synthetic data with real data for training not only enhances the robustness of the training process but also helps further optimize model performance. These findings confirm the potential of our approach in the application of object detection using overhead fisheye lenses. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
111971008 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111971008 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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