confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,lev...
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confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning *** garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological *** of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific *** fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this *** current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level *** conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object *** also compile and provide detailed descriptions ofwidely used datasets relevant to these detection *** endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection ***,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged objectdetection.
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