Publications

@article{sapkota_object_2026,

title = {Object detection with multimodal large vision-language models: An in-depth review},

author = {Ranjan Sapkota and Manoj Karkee},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525006475},

doi = {10.1016/j.inffus.2025.103575},

issn = {1566-2535},

year  = {2026},

date = {2026-02-01},

urldate = {2026-02-01},

journal = {Information Fusion},

volume = {126},

pages = {103575},

abstract = {The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. Furthermore, this review presents comprehensive visualizations demonstrating LVLMs’ effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review analysis, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. However, because of the unique and complimentary characteristics of traditional deep learning approaches and LVLMS, it is anticipated that hybrid approaches integrating both types of object detection models will be utilized in the future to maximize the speed, reliability and robotiness of the systems. Moreover, the review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and automated applications in the future.},

keywords = {Information fusion, Language and vision fusion, Large language models, Object detection, Vision-language models},

pubstate = {published},

tppubtype = {article}

}

@article{sapkota_comprehensive_2026,

title = {Comprehensive performance evaluation of YOLOv12, YOLO11, YOLOv10, YOLOv9 and YOLOv8 on detecting and counting fruitlet in complex orchard environments},

author = {Ranjan Sapkota and Zhichao Meng and Martin Churuvija and Xiaoqiang Du and Zenghong Ma and Manoj Karkee},

url = {https://www.sciencedirect.com/science/article/pii/S2949798126000050},

doi = {https://doi.org/10.1016/j.agrcom.2026.100125},

issn = {2949-7981},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Agriculture Communications},

volume = {4},

number = {1},

pages = {100125},

abstract = {This study systematically conducted an extensive real-world evaluation of all configurations of You Only Look Once (YOLO)-based object detection algorithms, including YOLOv8, YOLOv9, YOLOv10, YOLO11, and YOLOv12. Models were assessed using precision, recall, mean Average Precision at 50 % Intersection over Union (mAP@50), and computational efficiency across pre-processing, inference, and post-processing stages for detecting immature green fruitlets in commercial orchards. Field-level fruitlet counting was also validated using images captured with both Intel RealSense and iPhone 14 Pro Max sensors. YOLOv12l achieved the highest recall (0.900), while YOLOv10x and YOLOv9 GELAN-c reported the top precision scores of 0.908 and 0.903, respectively. YOLOv9 GELAN-base and GELAN-e achieved the highest mAP@50 (0.935), followed by YOLO11s (0.933) and YOLOv12l (0.931). In counting validation, YOLO11n demonstrated superior accuracy, with RMSE values of 4.51\textendash4.96 and MAE values of 3.85\textendash7.73 across four apple varieties. Sensor-specific training on Intel RealSense further improved detection performance. YOLO11n also recorded the fastest inference speed (2.4 ms), outperforming YOLOv8n, YOLOv9 GELAN-s, YOLOv10n, and YOLOv12n, affirming its suitability for real-time orchard applications.},

keywords = {agricultural automation, Fruitlet detection, Object detection, YOLO comparison, You Only Look Once (YOLO)},

pubstate = {published},

tppubtype = {article}

}