Improved object detection approach employing adaptive padding and Viola-Jones algorithm
| dc.authorid | KAYA, Yasin/0000-0002-9074-0189 | |
| dc.contributor.author | Dinc, Baris | |
| dc.contributor.author | Kaya, Yasin | |
| dc.date.accessioned | 2026-02-27T07:33:29Z | |
| dc.date.available | 2026-02-27T07:33:29Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | In recent years, significant advancements have been achieved in the field of object detection (OD) thanks to the rapid developments in deep learning (DL) methodologies. Although single-stage detectors have accelerated OD, the presence of high-frequency noise and irrelevant details makes these models sensitive to background noise. This challenge causes various difficulties, such as increased false-positive rates in the training process and computational costs. To address these limitations, we present a novel approach that improves the object detector's detection capability by reducing high-frequency noise in the input image. Specifically, the proposed model comprises a novel adaptive padding (AP) mechanism and a region of interest (RoI) detector to provide a balance between RoI generation and OD. To improve the ability to detect multi-scale objects within complex scenes, the model is separately trained with a RoI detector that combines a set of weak candidate regions into a single frame and then enhances its flexibility using the AP mechanism. To create a distinct contrast between the object regions and the background, a low-pass filter is finally applied to the input image. The proposed model was used for You Only Look Once version 8 (YOLOv8) and tested with 351 images of six bird species. It is essential to note that the proposed model outperformed the baseline YOLOv8, achieving superior detection performance in terms of accuracy, classification, and localization loss, while minimizing the high-frequency noise generated by redundant sub-images. The model increased the mean average precision calculated at an intersection over union (IoU) threshold of 0.50 (mAP50) from 0.954 to 0.984 and the mAP50-95 from 0.633 to 0.781. The proposed approach was also evaluated under domain shift conditions and indicated strong robustness across different data distributions. Moreover, the region proposal-based detection results showed more balanced and accurate classification results, with a reduction in the misclassified sample rate. Finally, validation with the state-of-the-art YOLOv12 further demonstrated superior performance, with gains exceeding 3% in mAP50 and 20% in mAP50-95, underscoring the scalability and robustness of the proposed approach. | |
| dc.identifier.doi | 10.7717/peerj-cs.3455 | |
| dc.identifier.issn | 2376-5992 | |
| dc.identifier.uri | http://dx.doi.org/10.7717/peerj-cs.3455 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14669/4602 | |
| dc.identifier.volume | 11 | |
| dc.identifier.wos | WOS:001652561500001 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.language.iso | en | |
| dc.publisher | PeerJ Inc | |
| dc.relation.ispartof | Peerj Computer Science | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_20260302 | |
| dc.subject | Object detection | |
| dc.subject | Image processing | |
| dc.subject | Viola-Jones algorithm | |
| dc.subject | Adaptive padding | |
| dc.subject | Gaussian blurring | |
| dc.subject | YOLO | |
| dc.title | Improved object detection approach employing adaptive padding and Viola-Jones algorithm | |
| dc.type | Article |
