Chen Bing's repositories
ThunderNet-Review
Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.
TensorMask-Review
Sliding-window object detectors that generate boundingbox object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense slidingwindow instance segmentation, which is surprisingly underexplored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.
MPU6050_Kalman_PWM_remote
MPU6050 Kalman PWM_remote
SLAM-for-Matlab
SLAM related matlab
SLAM-for-Paper
SLAM related papers and mathematical materials
Matlab-From-Zero-To-One
MATLAB quickstart
ethz_asl_UAV_autonomous
苏黎世理工开源的整套自主无人机系统
Multi-Sensor-Fusion
Multi-Sensor Fusion (GNSS, IMU, Camera and so on) 多源多传感器融合定位 GPS/INS组合导航
awesome-visual-slam
:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
VisualOdometry_BasedOnSURF
%% Estimating the pose of the second view relative to the first view %% Bootstrapping estimating camera trajectory using global bundle adjustment %% Estimating remaining camera trajectory using windowed bundle adjustment
Cpp-Primer
C++ Primer 5 answers
gpsSignalAcquisition
Example of Acquisition of the GPS C/A code
android_hal_gpsbds
A HAL driver for android4.x 5.x 6.x, support both GPS and BDS.
learnopencv
Learn OpenCV : C++ and Python Examples
hdl_graph_slam
3D LIDAR-based Graph SLAM
libpng
libviso2中使用的libpng++库
limo
Lidar-Monocular Visual Odometry
maskscoring_rcnn
Codes for paper "Mask Scoring R-CNN".
OpenSimpleLidar
Open Source scanning laser rangefinder
SLAM_Recent_Research
Track Advancement of SLAM 跟踪SLAM前沿动态【ICRA2019已更】