On-Road Deer Detection for Advanced Driver Assistance using Convolutional Neural Network

Abstract

Animal-vehicle collision (AVC) is a major concern in road safety that affects human life, properties, and wildlife. Most of the collisions happen with large animals especially deer that enters the road suddenly. Furthermore, the threat is even more alarming in poor visibility conditions such as night-time, fog, rain, etc. Therefore, it is vital to detect the presence of deer on roadways to mitigate the severity of deer-vehicle collision (DVC). This paper presents an efficient methodology to detect deer on roadways both during the day and night-time conditions using deep learning framework. A two-class CNN model differentiating a deer from its background is developed. The background will have a few classes of objects such as motorcycles, cars, and trees which are frequently encountered on roadways. A selfconstructed dataset with both RGB and thermal images is used to train the CNN model. Sliding window technique is used to localize the spatial region of deer in an image. The performance of the proposed CNN model is compared with state-of-the art classifiers and pre-trained CNN models and the results validate its effectiveness.

Wolves make roadways safer, generating large economic returns to predator conservation

“…{ Of these strategies, most are cost effective (meaning, the present value of benefits exceeds the present value of costs) only if deployed in DVC hotspots (59); hence, information and funding constraints limit the widespread use of mitigation strategies not involving wolves. Newer technologies, such as machine learning models that identify the presence of deer on roads (60,61) and rear-facing illumination systems that make vehicles more visible to deer (62), have shown promise in reducing DVCs. It remains unclear how quickly and widely these technologies can be deployed.…”

Kansrijke maatregelen om aanrijdingen met hoefdieren te voorkomen op de Zeeweg (N200) : Een literatuuronderzoek

“…Jino Hans et al (2020) presenteren een methode om met behulp van conventionele en thermische camerabeelden die vanuit een voertuig worden gemaakt, herten op de weg te detecteren. De methode, waarbij gebruik is gemaakt van kunstmatige intelligentie en waarbij beelden van diverse hertensoorten uit verschillende delen van de wereld zijn gebruikt, blijkt effectief in het herkennen van herten.…”

Wild Animal Detection System

“…The creation of such a tailored data collection using the COCO and Google Open Images datasets is described in the study. [6] With the use of machine learning algorithms, this methodology enables the features extraction of specific image regions and the classification of those regions into two categories: animal and non-animal. Five different ways were used to navigate the image's pixels as we compared different methods utilizing synthetic images.…”