17 research outputs found
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
With the demand for standardized large-scale livestock farming and the
development of artificial intelligence technology, a lot of research in area of
animal face recognition were carried on pigs, cattle, sheep and other
livestock. Face recognition consists of three sub-task: face detection, face
normalizing and face identification. Most of animal face recognition study
focuses on face detection and face identification. Animals are often
uncooperative when taking photos, so the collected animal face images are often
in arbitrary directions. The use of non-standard images may significantly
reduce the performance of face recognition system. However, there is no study
on normalizing of the animal face image with arbitrary directions. In this
study, we developed a light-weight angle detection and region-based
convolutional network (LAD-RCNN) containing a new rotation angle coding method
that can detect the rotation angle and the location of animal face in
one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a
single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset
including goat dataset and gaot infrared image. Evaluation result show that the
AP of face detection was more than 95% and the deviation between the detected
rotation angle and the ground-truth rotation angle were less than 0.036 (i.e.
6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent
performance on livestock face and its direction detection, and therefore it is
very suitable for livestock face detection and Normalizing. Code is available
at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/Comment: 8 figures, 5 table
Adaptive Rotated Convolution for Rotated Object Detection
Rotated object detection aims to identify and locate objects in images with
arbitrary orientation. In this scenario, the oriented directions of objects
vary considerably across different images, while multiple orientations of
objects exist within an image. This intrinsic characteristic makes it
challenging for standard backbone networks to extract high-quality features of
these arbitrarily orientated objects. In this paper, we present Adaptive
Rotated Convolution (ARC) module to handle the aforementioned challenges. In
our ARC module, the convolution kernels rotate adaptively to extract object
features with varying orientations in different images, and an efficient
conditional computation mechanism is introduced to accommodate the large
orientation variations of objects within an image. The two designs work
seamlessly in rotated object detection problem. Moreover, ARC can conveniently
serve as a plug-and-play module in various vision backbones to boost their
representation ability to detect oriented objects accurately. Experiments on
commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our
proposed ARC module in the backbone network, the performance of multiple
popular oriented object detectors is significantly improved (e.g. +3.03% mAP on
Rotated RetinaNet and +4.16% on CFA). Combined with the highly competitive
method Oriented R-CNN, the proposed approach achieves state-of-the-art
performance on the DOTA dataset with 81.77% mAP
Multi-Grained Angle Representation for Remote Sensing Object Detection
Arbitrary-oriented object detection (AOOD) plays a significant role for image
understanding in remote sensing scenarios. The existing AOOD methods face the
challenges of ambiguity and high costs in angle representation. To this end, a
multi-grained angle representation (MGAR) method, consisting of coarse-grained
angle classification (CAC) and fine-grained angle regression (FAR), is
proposed. Specifically, the designed CAC avoids the ambiguity of angle
prediction by discrete angular encoding (DAE) and reduces complexity by
coarsening the granularity of DAE. Based on CAC, FAR is developed to refine the
angle prediction with much lower costs than narrowing the granularity of DAE.
Furthermore, an Intersection over Union (IoU) aware FAR-Loss (IFL) is designed
to improve accuracy of angle prediction using an adaptive re-weighting
mechanism guided by IoU. Extensive experiments are performed on several public
remote sensing datasets, which demonstrate the effectiveness of the proposed
MGAR. Moreover, experiments on embedded devices demonstrate that the proposed
MGAR is also friendly for lightweight deployments.Comment: 13 pages, 9 figures, 14 table