A systematic review of 60 strawberry detection studies finds that 35 percent applied data augmentation before dataset ...
Underwater object detection is challenged by light attenuation, scattering, and severe appearance degradation, which introduce instability across different stages of the detection pipeline. To address ...
Researchers integrated an InceptionV3 convolutional neural network into the YOLOv8 object detection architecture to achieve ...
Object detection in deep learning encompasses a range of approaches that identify and localise instances of semantic classes in images. Early methods relied on two-stage architectures, first ...
Using yolov3 & yolov4 weights objects are being detected from live video frame along with the measurement of the object from the camera without the support of any extra hardware device. This project ...
The novel technique is based on the VarifocalNet deep-learning object detection framework, which was reportedly tweaked to achieve quicker and more accurate results. Compared to other such methods, ...
A comparison of the number of research papers in two types of COD, traditional and deep learning, within Image-level and Video-level applications. The bar chart shows the increase in overall research ...
Umashanker Bandari’s co-authored IEEE Xplore paper explores a context-aware deep learning framework for accurate, efficient, ...
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