An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

Real-time vehicle detection using low-cost sensors

Improving road safety and reducing the number of accidents is one of the top priorities for the automotive industry. As human driving behaviour is one of the top causation factors of road accidents, research is working towards removing control from the human driver by automating functions and finally introducing a fully Autonomous Vehicle (AV). A Collision Avoidance System (CAS) is one of the key safety systems for an AV, as it ensures all potential threats ahead of the vehicle are identified and appropriate action is taken. This research focuses on the task of vehicle detection, which is the base of a CAS, and attempts to produce an effective vehicle detector based on the data coming from a low-cost monocular camera. Developing a robust CAS based on low-cost sensor is crucial to bringing the cost of safety systems down and in this way, increase their adoption rate by end users. In this work, detectors are developed based on the two main approaches to vehicle detection using a monocular camera. The first is the traditional image processing approach where visual cues are utilised to generate potential vehicle locations and at a second stage, verify the existence of vehicles in an image. The second approach is based on a Convolutional Neural Network, a computationally expensive method that unifies the detection process in a single pipeline. The goal is to determine which method is more appropriate for real-time applications. Following the first approach, a vehicle detector based on the combination of HOG features and SVM classification is developed. The detector attempts to optimise performance by modifying the detection pipeline and improve run-time performance. For the CNN-based approach, six different network models are developed and trained end to end using collected data, each with a different network structure and parameters, in an attempt to determine which combination produces the best results. The evaluation of the different vehicle detectors produced some interesting findings; the first approach did not manage to produce a working detector, while the CNN-based approach produced a high performing vehicle detector with an 85.87% average precision and a very low miss rate. The detector managed to perform well under different operational environments (motorway, urban and rural roads) and the results were validated using an external dataset. Additional testing of the vehicle detector indicated it is suitable as a base for safety applications such as CAS, with a run time performance of 12FPS and potential for further improvements.</div