DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

  title={DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection},
  author={Zhanchao Huang and Jianlin Wang},

Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection

Dilated spatial pyramid-You only look once model outperforms other state-of-the-art methods in mean average precision, while it still keeps a satisfying real-time detection speed.

PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

A novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly and proposes attention mechanisms in spatial and channel dimensions to adaptively assign the importance of features in different scales.

Improved Pedestrian Detection Algorithm of Yolov4 Network Structure

Experimental results show that the YOLOV4 network has a better effect on pedestrian detection, and the accuracy and average precision are improved.

Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion

Comparative experimental results prove that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets and achieves a high real-time performance.

Toward Structural Learning and Enhanced YOLOv4 Network for Object Detection in Optical Remote Sensing Images

  • Kun WangM. Liu
  • Environmental Science
    Advanced Theory and Simulations
  • 2022
With the maturity of technological tools such as satellites and airplanes, object detection in optical remote sensing images have been widely used in military and civilian fields. Due to the

Evaluation of Robust Spatial Pyramid Pooling Based on Convolutional Neural Network for Traffic Sign Recognition System

This paper investigates the state-of-the-art of various object detection systems (Yolo V3, Resnet 50, Densenet, and Tiny YoloV3) combined with spatial pyramid pooling (SPP), and shows that Yolo V 3 SPP strikes the best total BFLOPS, mAP, and mAP measures, and SPP can improve the performance of all models in the experiment.

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

The proposed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO) approach achieved better performance than traditional Y OLO-V3 and other state-of-the-art models for remote sensing target detection.

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

This article proposes an effective lightweight oriented object detector (LO-Det), and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity.

YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images

This work introduces an enhanced one-stage deep learning-based detection model, called You Only Look Once (YOLO)-fine, which is based on the structure of YOLOv3, designed to be capable of detecting small objects with high accuracy and high speed, allowing further real-time applications within operational contexts.

Vehicle detection method based on adaptive multi-scale feature fusion network

The proposed adaptive multi-scale feature fusion network (AMFFN) fuses features of multiple scales across layers and assigns learnable weights to layers of different scales and depthwise separable convolution is used to replace the normal convolution and increase the speed of detection.



Feature Pyramid Networks for Object Detection

This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.

R-FCN++: Towards Accurate Region-Based Fully Convolutional Networks for Object Detection

This paper involves Global Context Module to improve the classification score maps by adopting large, separable convolutional kernels and introduces a new pooling method to better extract scores from the score maps, by using row-wise or column-wise max pooling.

R-FCN: Object Detection via Region-based Fully Convolutional Networks

This work presents region-based, fully convolutional networks for accurate and efficient object detection, and proposes position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi- scale object detection, which is learned end-to-end, by optimizing a multi-task loss.

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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.

Enhancement of SSD by concatenating feature maps for object detection

The proposed network is suitable for sharing the weights in the classifier networks, by which property, the training can be faster with better generalization power, and shows state-of-the-art mAP, which is better than those of the conventional SSD, YOLO, Faster-RCNN and RFCN.

Scale-Transferrable Object Detection

A novel Scale-Transferrable Detection Network (STDN) for detecting multi-scale objects in images is developed, equipped with embedded super-resolution layers (named as scale-transfer layer/module in this work) to explicitly explore the interscale consistency nature across multiple detection scales.

Boosted local structured HOG-LBP for object localization

This paper proposes a boosted Local Structured HOG-LBP based object detector to capture the object's local structure, and develop the descriptors from shape and texture information, respectively, and presents a boosted feature selection and fusion scheme for part based object detectors.