arXiv subjects

Ismail Ismail Tijjani

Publications and source records attributed to Ismail Ismail Tijjani.

5 recordsLinked to original sources

arXiv2026

NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction

Respiratory motion degrades abdominal T2 HASTE MRI in low- and middle-income countries where vendor motion-correction licenses are unavailable and failed scans are deleted during routine PACS cleanup, precluding supervised training. To address this, we introduce NIMARC-MRI, the first public abdominal MRI dataset from West Africa, comprising 139 clean HASTE, 25 native motion-degraded HASTE, and 79 paired HASTE-TSE TRIGGER acquisitions from a Nigerian centre operating 1.5 T Siemens scanner without integrated motion-correction licenses. A 2D U-Net with 7.7 million parameters was trained on 3D-consistent synthetic respiratory motion necessitated by local infrastructure reality and validated via a three-tier strategy: held-out synthetic data, blinded radiologist Likert scoring on native real motion, and cross-sequence TRIGGER generalisation. On synthetic test data the model achieved SSIM 0.863 +/- 0.042 and PSNR 30.06 +/- 1.80 dB. On native real motion, however, blinded radiologist and radiographer scores showed no significant improvement (mean Likert 4.06 +/- 0.55 original versus 4.04 +/- 0.61 corrected, P = 0.914), with 28% of cases rated worse after correction. Cross-sequence TRIGGER evaluation showed modest SSIM improvement (0.404 +/- 0.073 versus 0.334 +/- 0.055, P < 0.001) without radiologist-perceived gain. These findings demonstrate that conservative synthetic motion fails to capture clinical motion severity, exposing a reproducible synthetic-to-real gap. NIMARC-MRI is released on Zenodo (https://doi.org) under a controlled data-use agreement requiring citation. Sequence metadata and a sample subset are publicly accessible to facilitate discovery, while patient-level data remain restricted to approved collaborators. The aim is to establish a reproducible baseline for motion correction in resource-constrained settings.

eess.IV↗

arXiv2026

A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa

The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming. However, many existing approaches rely on controlled datasets that do not adequately represent realworld farming conditions, particularly in underrepresented regions such as Africa. This study presents a comparative evaluation of six object detection models YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR using a real-world dataset, AgriAISeg 1 , collected manually from Nigerian farms. AgriAISeg comprises 3,382 images of sesame, cabbage, and tomato crops captured under varying environmental conditions, including changes in illumination, occlusion, and viewing perspectives. Models were trained, and performance was assessed using precision, recall, mAP@0.5, and mAP@0.5:0.95. The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624, while YOLOv8 and YOLO11 also demonstrated strong and consistent performance. In contrast, Faster R-CNN recorded significantly lower accuracy, with an overall mAP@0.5 of 0.466, indicating reduced effectiveness under complex field conditions. In addition, YOLO-based models exhibited superior training efficiency compared to Faster R-CNN.These findings demonstrate that modern one-stage and transformer-based detectors provide more reliable and efficient solutions for plant detection in realworld agricultural environments.

cs.CV↗

arXiv2026

Evaluating Vision-Language Models as a Zero-Shot Learning Alternative to You Only Look Once and Optical Character Recognition for Nigerian License Plate Recognition

License Plate Recognition (LPR) systems are critical tools in traffic monitoring, security enforcement, and urban mobility management. Traditional LPR systems often rely on a multi-stage pipeline involving object detection using You Only Look Once (YOLO) and Optical Character Recognition (OCR), which suffer from limitations such as high resource demands, poor performance in unstructured environments, and the need for large annotated datasets. This study explores the potential of Vision-Language Models (VLMs) as a unified, zeroshot learning solution for Nigerian license plate recognition. Using a curated dataset of 88 challenging real-world images collected in Nigeria, we evaluate five selected VLMs: Gemini 2.0 Flash Exp (Google DeepMind), Qwen2.5-VL-7B-Instruct (Alibaba), GPT-4o (OpenAI), Claude 4 Sonnet (Anthropic), and Llama 3.2 Vision 90b (Meta). Results based on Character Error Rate (CER) reveal that Gemini and Qwen significantly outperform other models in both accuracy and robustness, on the challenging image scenarios. This work highlights the practical advantages of VLMs over YOLO+OCR, questions the claims by model providers, and compares the performances of the VLMs.

cs.CV↗

arXiv2026

Sesame Plant Segmentation Dataset: A YOLO Formatted Annotated Dataset

This paper presents the Sesame Plant Segmentation Dataset, an open source annotated image dataset designed to support the development of artificial intelligence models for agricultural applications, with a specific focus on sesame plants. The dataset comprises 206 training images, 43 validation images, and 43 test images in YOLO compatible segmentation format, capturing sesame plants at early growth stages under varying environmental conditions. Data were collected using a high resolution mobile camera from farms in Jirdede, Daura Local Government Area, Katsina State, Nigeria, and annotated using the Segment Anything Model version 2 with farmer supervision. Unlike conventional bounding box datasets, this dataset employs pixel level segmentation to enable more precise detection and analysis of sesame plants in real world farm settings. Model evaluation using the Ultralytics YOLOv8 framework demonstrated strong performance for both detection and segmentation tasks. For bounding box detection, the model achieved a recall of 79 percent, precision of 79 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 58 percent. For segmentation, it achieved a recall of 82 percent, precision of 77 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 52 percent. The dataset represents a novel contribution to sesame focused agricultural vision datasets in Nigeria and supports applications such as plant monitoring, yield estimation, and agricultural research.

cs.CV↗

arXiv2024

Performance Comparison of Deep Learning Techniques in Naira Classification

The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.

cs.CV↗