A quantitative detection algorithm based on improved faster R-CNN for marine benthos

TL;DR: Wang et al. as discussed by the authors reconstructed the MBConvBlock by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer and removed the fully connected layer of the attention module and convolution is used to cut down the amount of network parameters.

TL;DR: In this article , a convolutional block attention module was added to the backbone network to enhance and extract critical features of leaf images by fusing cross-channel information and spatial information, and the DIoU-NMS algorithm was adopted to modify the regional proposal network by replacing the original NMS.

TL;DR: Deep learning-based visual detection of marine organisms (VDMO) has attracted rapidly increasing attention paid to marine organisms, thereby expected to significantly benefit ocean ecology as discussed by the authors , and a comprehensive survey of deep learning based VDMO techniques are comprehensively revisited from a systematic viewpoint.

TL;DR: In this paper , a deep learning object detection algorithm based on YOLOv7 was used to design a new network, called Underwater-YOLO-v7 (U-YoolOv 7), for underwater organism detection.

TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

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: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.

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.