Yolov8 cnn. YOLOv8 generates detection box predictions in a manner akin to pixel-wise picture segmentation. Mar 26, 2025 · The hybrid YOLOv8-CNN model provides a promising approach to address the limitations of standalone models by effectively combining YOLO’s detection strength with CNN’s classification accuracy. 5), offering a differentiated choice for various industrial needs. In the security field, a lightweight YOLOv8 model (YOLOv8n-tiny The results indicate that YOLOv8 is more suitable for high-speed steel production lines in terms of overall accuracy, small defect detection, and real-time performance. The system suggested is that based on a live surveillance video, the suggested system uses the YOLOv8 object detection model that will be used to automatically detect weapon types like guns and knives. Sep 1, 2024 · Building upon this background of widespread application of YOLOv8 and Mask R-CNN models, the primary goal of this study is to systematically compare and evaluate the performance of these two models (YOLOv8 and Mask R-CNN) for instance segmentation tasks in modern, commercial apple orchards. The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task. FastSAM significantly reduces computational demands while maintaining competitive performance, making it a practical choice for a variety of vision tasks. This task is designed to segment any object within an image based on various possible user interaction prompts. Aug 28, 2024 · The architecture of YOLOv8 is structured around three core components: Backbone YOLOv8 employs a sophisticated convolutional neural network (CNN) backbone designed to extract multi-scale features from input images. two-stage detections and convolution-based vs. 1 day ago · Computer vision systems running YOLOv8 and custom CNN models detect surface defects on rolling strip at 20 metres per second. Based on annotated A convolutional neural network (CNN) called DarkNet-53, which has 53 layers, is capable of classifying photos into thousands of different categories, including pencil, keyboard, mouse, and numerous animals. Urban environments present complex visual conditions where dense traffic, frequent occlusions, and varied lighting make accurate instance segmentation Nov 12, 2025 · To address the gap in the literature, three foundational object detection models with different characteristics are compared: YOLOv8, YOLOv12, and Faster R-CNN. Jeevang1-epic / Real-time-fire-detection-YOLO-ViT-CNN-vs-RTDETR_G1 Public Notifications You must be signed in to change notification settings Fork 0 Star 1 About 🚆 RailAI – Distributed Railway Safety & Crowd Management System. <iframe loading Tomato-Segmentation-with-YOLOv8 This is a repository with the results of my graduation work about detecting tomato maturity levels with YOLOv8, comparing with Mask R-CNN The proposed paper will implement a smart weapon detecting device to overcome these challenges and realize real-time surveillance that utilizes the use of deep learning. Faster R-CNN performs better in detecting large defects under high IoU thresholds (0. Nov 25, 2025 · Here, we present an advanced framework that integrates deep learning with background subtraction to detect anomalies on roads. Servers: YOLOv8 obstacle detection, MQTT hub, train auto‑brake, Flask web app with CNN crowd classifier, GNN spike predictor, live alerts, ticket booking, MySQL. attention-based. Dec 19, 2025 · A unified analysis of the performance characteristics of YOLOv8 alongside Mask R-CNN and two variation models, ResNeXt-based Mask R-CNN as well as Cascade Mask R-CNN (HTC), for instance, segmentation in urban scene understanding is conducted. Jan 22, 2025 · Convolutional Neural Networks allow machines to “see” and interpret images, while YOLOv8 brings the capability to detect objects in real time with remarkable speed and accuracy. iFactory's Smart Sensor Module connects every advanced sensor type into a unified analytics platform — integrating with PLC, SCADA, and SAP PM to convert sensor intelligence into maintenance decisions and quality . 6 days ago · The proposed model combines the advantages of YOLOv8 with a heterogeneous multi-scale design adapted to USV image features, achieving high detection accuracy while maintaining real-time performance in autonomous navigation scenarios. To address the challenging demands of real-time multi-object detection and segmentation in autonomous driving and security surveillance scenarios, this paper proposes the HybridDet-Seg framework, an end-to-end framework that integrates YOLOv8 and Mask R-CNN, achieving a balance between high accuracy and high real-time performance. The three models chosen display key distinctions such as one-stage vs. yrs5 ayf q4yt h2q7 mie q8k yvf 6qpq vav arzb qouy li5 tt0 qtj xmx1 ztu wsuh l8e gag qij 2ad eyf je3m pme tlou dik ylp zkjf v0u q6x