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All Journal EMITTER International Journal of Engineering Technology
Ullah Sheikh, Usman
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Computer Science & IT

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FloYO-Net: Enhancing Small Floating Waste Detection in Natural Waters Using Atrous YOLOv5s Badams, Badiu; Ullah Sheikh, Usman; Syed Abu Bakar, Syed Abd Rahman; Abdul Wahab, Norhaliza
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.978

Abstract

Detecting small and partially hidden objects in rivers and water bodies remains a major challenge for real-time waste detection systems. These objects are often missed due to their small size, low contrast, and cluttered surroundings. Further complicating the task is the lack of dedicated datasets focused on small floating debris, limiting the development of more capable detection models. To bridge this gap, we developed D_six, a custom dataset of 495 high-resolution images capturing six classes of floating waste under real-world conditions. In this study, we improve the YOLOv5s object detection model by integrating atrous convolutions at three key backbone layers: P1/2, P3/8, and P5/32. These layers represent different scales of the feature pyramid, and the strategic placement of atrous convolution at each level plays a crucial role in helping the model recognize small and occluded objects more effectively. Using a dilation rate of 6, the model’s receptive field is expanded without increasing its size or slowing it down. When trained and evaluated on the D_six data set, the FloYO-Net (Floating Object YOLO Network) consistently outperformed the standard YOLOv5s, achieving a mean Average Precision (mAP@0.5) of 0.828 and mAP@0.5:0.95 of 0.509, compared to 0.787 and 0.498 respectively. Improvements were especially notable for hard-to-detect items like plastic bottles and plastic drink containers, with average precision gains of 6.6% and 7.1%, respectively. These results demonstrate that atrous convolution — when thoughtfully placed — can significantly improve detection accuracy, making it a powerful enhancement for real-time environmental cleanup systems.
Towards Robust Recognition of Handwritten Arabic Characters with Diacritics Using an Incremental Learning Approach Based on CNNs Shugaba, Fatima Aliyu; Ullah Sheikh, Usman; Othman, Mohd Afzan; Khamis, Nurulaqilla; Abdulfattah, Muhammad Habibullah
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.982

Abstract

Handwritten Arabic text recognition (HATR) presents unique challenges due to complex character shapes, contextual variations, cursive connections, and the presence of diacritical marks. This study introduces AHAD (Arabic Handwritten Alphabet with Diacritics), a novel benchmark dataset of 71,061 handwritten Arabic character images annotated with five primary vowel diacritics; Fathah, Kasrah, Dammah, Shaddah, and Sukoon, covering 492 distinct classes that combine character identity, contextual form, and diacritic. Leveraging this dataset, we propose an incremental learning framework based on Convolutional Neural Networks (CNNs) to address fine-grained recognition of handwritten Arabic characters with its corresponding diacritics. The model was initially trained on a 114-class dataset of handwritten Arabic characters (in all contextual forms) of non-diacritic characters and fine-tuned in two phases using the AHAD dataset. The two-phase strategy includes output layer expansion, learning rate adjustment, and gradual unfreezing of deeper layers to enhance knowledge retention and prevent catastrophic forgetting. The proposed method achieved a validation accuracy of 92.96% and a test accuracy of 93.26%. Our findings demonstrate the effectiveness of incremental learning for diacritic-aware Arabic handwriting recognition and establish AHAD as a strong baseline for future research in this field.
Co-Authors Abdulfattah, Muhammad Habibullah Badams, Badiu Khamis, Nurulaqilla Norhaliza Abdul Wahab, Norhaliza Othman, Mohd Afzan Shugaba, Fatima Aliyu Syed Abu Bakar, Syed Abd Rahman