Understanding of Object Detection Based on CNN Family and YOLO

By Juan Du. 2018

399 citations recorded by the source corpus.

As a key use of image processing, object detection has boomed along with the unprecedented advancement of Convolutional Neural Network (CNN) and its variants since 2012. When CNN series develops to Faster Region with CNN (R-CNN), the Mean Average Precision (mAP) has reached 76.4, whereas, the Frame Per Second (FPS) of Faster R-CNN remains 5 to 18 which is far slower than the real-time effect. Thus, the most urgent requirement of object detection improvement is to accelerate the speed. Based on the general introduction to the background and the core solution CNN, this paper exhibits one of the best CNN representatives You Only Look Once (YOLO), which breaks through the CNN family’s tradition and innovates a complete new way of solving the object detection with most simple and high efficient way. Its fastest speed has achieved the exciting unparalleled result with FPS 155, and its mAP can also reach up to 78.6, both of which have surpassed the performance of Faster R-CNN greatly. Additionally, compared with the latest most advanced solution, YOLOv2 achieves an excellent tradeoff between speed and accuracy as well as an object detector with strong generalization ability to represent the whole image.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ddd3781687a82d329f0f2b9fa32aa85cedfd93b6

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In Computer Vision and Pattern Recognition

Citations in the corpus

YOLOv11: An Overview of the Key Architectural Enhancements (2024, methodology): “It employs a single convolutional neural network to simultaneously predict bounding boxes and class probabilities across the entire image [6], streamlining the detection pipeline compared to more complex traditional methods.”
The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024, neutral): “The evolution of object detection methods has paved the way for YOLO, offering a novel solution to the longstanding challenge of balancing speed and accuracy in detection tasks [10,11].”