40 research outputs found
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
Deep learning enhanced mobile-phone microscopy
Mobile-phones have facilitated the creation of field-portable, cost-effective
imaging and sensing technologies that approach laboratory-grade instrument
performance. However, the optical imaging interfaces of mobile-phones are not
designed for microscopy and produce spatial and spectral distortions in imaging
microscopic specimens. Here, we report on the use of deep learning to correct
such distortions introduced by mobile-phone-based microscopes, facilitating the
production of high-resolution, denoised and colour-corrected images, matching
the performance of benchtop microscopes with high-end objective lenses, also
extending their limited depth-of-field. After training a convolutional neural
network, we successfully imaged various samples, including blood smears,
histopathology tissue sections, and parasites, where the recorded images were
highly compressed to ease storage and transmission for telemedicine
applications. This method is applicable to other low-cost, aberrated imaging
systems, and could offer alternatives for costly and bulky microscopes, while
also providing a framework for standardization of optical images for clinical
and biomedical applications