We present a simple yet effective learning technique that significantly improves mAP of
YOLO object detectors without compromising their speed. During network training, we …

Object detection is a fundamental task in computer vision. To improve the detection accuracy
of a detection model without increasing the model weights, this paper modifies the YOLOX …

Object detection is one of the most important areas in computer vision, which plays a key
role in various practical scenarios. Due to limitation of hardware, it is often necessary to …

Object detection in real images is a challenging problem in computer vision. Despite several
advancements in detection and recognition techniques, robust and accurate localization of …

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

Research on deep learning has always had two main streams:(1) design a powerful network
architecture and train it with existing learning methods to achieve the best results, and (2) …

Object detection is a valuable but challenging technology in computer vision research.
Although existing methods could attain satisfactory results on high-performance computers …

We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection
models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU …

Real-time scene parsing through object detection running on an embedded device is very
challenging, due to limited memory and computing power of embedded devices. To deal …

We show that the YOLOv4 object detection neural network based on the CSP approach,
scales both up and down and is applicable to small and large networks while maintaining …