Optimized Reward Function Based Deep Reinforcement Learning Approach for Object Detection Applications
Yazarlar (2)
Fırat Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum |
| DOI Numarası | 10.1109/DASA54658.2022.9764979 |
| Bildiri Dili | İngilizce |
| Kongre Adı | 2022 International Conference on Decision Aid Sciences and Applications (DASA) |
| Kongre Tarihi | 23-03-2022 / |
| Basıldığı Ülke | Tayland |
| Basıldığı Şehir | Chiang-Rai |
| Bildiri Linki | https://ieeexplore.ieee.org/abstract/document/9764979 |
| Özet |
| Reinforcement learning is considered a powerful artificial intelligence method that can be used to teach machines through interaction with the environment and learning from their mistakes. More and more applications are coming to the fore where Reinforcement learning has been newly and successfully implemented. It is frequently used especially in the game industry and robotics. In this article, a deep reinforcement learning approach, which uses our own developed neural network, is presented for object detection on the PASCAL Voc2012 dataset. Our approach is by moving a bounding box step-by-step towards the goal in order to fully frame the object in the picture. The created neural network consists of a 5-layer structure. In addition, it is aimed to maximize the mAP value by optimizing the reward function. The right choice in the reward policy will certainly affect the outcome and will play an important role in the … |
| Anahtar Kelimeler |
| Object detection | deep reinforcement learning | CNN | deep learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 3 |
| Google Scholar | 7 |