Company Filing History:
Years Active: 2022
Title: Peiming Ren: Innovator in Real-Time Object Detection
Introduction
Peiming Ren is a notable inventor based in Wuxi, China. He has made significant contributions to the field of deep learning and image processing, particularly in the development of real-time object detection methods. His innovative approach addresses the challenges of deploying advanced algorithms on platforms with limited computing resources.
Latest Patents
Peiming Ren holds a patent for a real-time target detection method deployed on a platform with limited computing resources. This invention improves the YOLO-v3-tiny neural network by introducing the Tinier-YOLO model. The Tinier-YOLO model retains the first five convolutional and pooling layers of YOLO-v3-tiny while making predictions at two different scales. The model size is only 7.9 MB, significantly smaller than its predecessors, yet it maintains real-time performance and accuracy. The real-time performance of Tinier-YOLO is 21.8% higher than YOLO-v3-tiny and 70.8% higher than YOLO-v2-tiny. Additionally, the accuracy of Tinier-YOLO is increased by 10.1% compared to YOLO-v3-tiny and nearly 18.2% compared to YOLO-v2-tiny.
Career Highlights
Peiming Ren is affiliated with Jiangnan University, where he continues to advance his research in artificial intelligence and image processing. His work has garnered attention for its practical applications in real-time detection systems.
Collaborations
Peiming Ren has collaborated with colleagues Wei Fang and Lin Wang, contributing to the development of innovative solutions in their field.
Conclusion
Peiming Ren's contributions to real-time object detection exemplify the potential of deep learning technologies in resource-constrained environments. His work not only enhances the efficiency of detection systems but also sets a benchmark for future innovations in the field.