Based Object
Based object detection focuses on accurately identifying and locating objects within images or video streams, a crucial task in computer vision with broad applications. Current research emphasizes improving the efficiency and accuracy of object detection, particularly using convolutional neural networks (CNNs) and spiking neural networks (SNNs), often incorporating techniques like template matching and multi-stage detection pipelines to handle challenges such as object size variation and image quality. These advancements are driving progress in diverse fields, including robotics (e.g., automated assembly), materials science (e.g., defect analysis), and autonomous systems (e.g., drone navigation), where reliable object detection is essential for effective operation. Ongoing efforts also concentrate on optimizing loss functions to better align with evaluation metrics like Average Precision (AP) for improved model performance.