Survey on object detection in VLSI architecture through deep learning.

Object detection is one of the most challenging problems in computer vision. Detecting an object from an image or video finds applications in different fields like security, medical, automated vehicle systems, etc. Locating instances from a real-world image requires improved accuracy and low latency. Deep learning (DL) techniques have emerged as a powerful tool for training the system and detecting instances. However, DL-based object detection for real-time applications requires a lot of computational resources. Field Programming Gate Array (FPGA) is found to be more promising to give optimized results for object detection using deep learning. This paper presents a detailed study of various DL-based models using different datasets like MS-COCO, PASCAL VOC, etc for object detection. It also provides good insight for selecting an algorithm for an object detection application with FPGA architecture. The study shows that the advancement in software and hardware technologies enables accurate and efficient detection systems for a wide range of applications. [ABSTRACT FROM AUTHOR]