Object detection has recently become a cornerstone task in computer vision. It allows the machine to identify and localise multiple objects in an image. With the development of deep learning, object detection systems have achieved remarkable performance in various domains such as autonomous driving, surveillance, healthcare and industrial automation. Among the various detection frameworks, the YOLO (You Only Look Once) family of models has attracted significant attention due to its unified architecture, real-time inference capabilities, and high detection accuracy \cite{redmon2016you}. YOLO models frame object detection as a single regression problem , directly from image pixels to bounding box coordinates and class probabilities . This makes them very fast and efficient for real world usage. The Artificial Intelligence evolution has quickly transitioned from Narrow Intelligence (ANI) to the present day of sophisticated deep learning and Transformers. As AI systems become increasingly autonomous and intelligent, the research community has shifted from pure accuracy to 'Explainable AI' (XAI) as pointed out in current trend reports \cite{singh2023introduction}. Our work is based on this global trend, by considering the YOLOv8 detector not as a simple output generator, but as a system to explain its detections to the user.
Noha ElMasry, Fahima A. Maghraby, Mohamed Waleed Fakhr· Zenodo (CERN European Organi...· 0 citations
Object detection has recently become a cornerstone task in computer vision. It allows the machine to identify and localise multiple objects in an image. With the development of deep learning, object detection systems have achieved remarkable performance in various domains such as autonomous driving, surveillance, healthcare and industrial automation. Among the various detection frameworks, the YOLO (You Only Look Once) family of models has attracted significant attention due to its unified architecture, real-time inference capabilities, and high detection accuracy \cite{redmon2016you}. YOLO models frame object detection as a single regression problem , directly from image pixels to bounding box coordinates and class probabilities . This makes them very fast and efficient for real world usage. The Artificial Intelligence evolution has quickly transitioned from Narrow Intelligence (ANI) to the present day of sophisticated deep learning and Transformers. As AI systems become increasingly autonomous and intelligent, the research community has shifted from pure accuracy to 'Explainable AI' (XAI) as pointed out in current trend reports \cite{singh2023introduction}. Our work is based on this global trend, by considering the YOLOv8 detector not as a simple output generator, but as a system to explain its detections to the user.
Noha ElMasry, Fahima A. Maghraby, Mohamed Waleed Fakhr· Zenodo (CERN European Organi...· 0 citations
DLC-OD is an object-detection dataset containing 295 hand-sketched digital logic circuit images collected from 20 participants. The dataset includes 1,203 manually annotated instances belonging to seven logic-gate classes: AND, NAND, NOR, NOT, OR, XNOR, and XOR. The images and bounding-box annotations are provided in YOLO format. In the deposited version, the dataset is divided into 206 training images containing 831 instances, 60 validation images containing 255 instances, and 29 test images containing 117 instances. The dataset was developed to support research on hand-drawn logic-gate detection, symbolic object recognition, explainable artificial intelligence, class activation mapping, and educational engineering applications. Drawing variations among participants provide differences in symbol shape, stroke thickness, orientation, and circuit layout.
Noha ElMasry, Fahima A. Maghraby, Mohamed Waleed Fakhr· Zenodo (CERN European Organi...· 0 citations
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