sunkangjian96's starred repositories
EC-Bestiary
A bestiary of evolutionary, swarm and other metaphor-based algorithms
Image-Downloader
Download images from Google, Bing, Baidu. 谷歌、百度、必应图片下载.
objectdetection_script
一些关于目标检测的脚本的改进思路代码，详细请看readme.md
SimpleCVReproduction
Replication of simple CV Projects including attention, classification, detection, keypoint detection, etc.
yolov3-point
Learning YOLOv3 from scratch 从零开始学习YOLOv3代码
HIT-UAV-Infrared-Thermal-Dataset
A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
cyclegan-keras
这是一个cyclegan-keras的源码，可以用于训练自己的模型。
dcgan-keras
这是一个dcgan-keras的源码，可以用于训练自己的模型。
pytorch-GAN
A minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks
srgan-keras
这是一个srgan-keras的源码，可以用于训练自己的模型。
srgan-pytorch
这是一个srgan-keras的源码，可以用于训练自己的模型。
cyclegan-pytorch
这是一个cyclegan-pytorch的源码，可以用于训练自己的模型。
dcgan-pytorch
这是一个dcgan-pytorch的源码，可以用于训练自己的模型。
Siamese-pytorch
这是一个孪生神经网络（Siamese network）的库，可进行图片的相似性比较。
Yolov7-tracker
Yolo X, v7, v8 and several Multi-Object Tracker(SORT, DeepSORT, ByteTrack, BoT-SORT, etc.) in MOT17 and VisDrone2019 Dataset. It uses a unified style and integrated tracker for easy embedding in your own projects.
open-alcnet
codes and trained models for the ALCNet
Small-bounding-box-filter-for-small-target-detection
In order to detect small targets under the condition of dense clutters, we propose a single-frame target detection algorithm based on a small bounding-box filter, which is characterized by good adaptability to the position and size of a small target. During the small target detection process, the proposed algorithm first searches for the local maximum gray pixel and then, a set of concentric bounding boxes whose center is the pixel found in the first step is constructed, and the detection thresholds of a neighboring region of this pixel are calculated based on the bounding boxes. Finally, the minimum threshold is used to detect small target pixels in the neighboring region. A fast version of the proposed algorithm is a minimum bounding-box filter, which can be implemented by dividing an image into blocks and using the mid-range and range to assess the concentration trend and dispersion of the background. Simulation and analysis results show that the proposed algorithm can achieve high detection probability and low false alarm rate when detecting small targets in the complex background; while its fast version has high computational efficiency. The proposed algorithm can be used in star tracker (refer to demo), infrared searching and tracking systems (refer to reference).
Infrared-Image-Processing-small-target
small target detection and track in infrared image and video