Apple detection during different growth stages in orchards using the improved YOLO-V3 model

TL;DR: In this article , an emerging image detection algorithm, YOLO-V3, is utilized to train the self-made dataset and classify the printed circuit boards (PCBs) into specific categories.

TL;DR: One-stage networks can effectively increase the detection speed as mentioned in this paper, which is a branch of regression-based object detection, which uses a single neural network to directly predict bounding boxes and class probabilities from the entire image by one evaluation.

TL;DR: A novel approach for extracting grape phenotypic traits from RGB videos captured by a UAV is presented. Multi-object tracking and segmentation algorithms are used to determine bunch and berry traits. Two datasets with labelled images and phenotyping measurements are created and made available. The results show that the detection and tracking algorithms perform adequately for bunch and berry detection and counting.

TL;DR: This study records the roads of various cities in Taiwan through road driving to create a data set of road signs in Taiwan in daytime environments as well as nighttime environments, and recommends using CLAHE's YOLOv5x as the best model at night and used to improve the effect of road sign detection at night.

TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.

TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.