Encounters between humans and wildlife often lead to injuries, especially in remote wilderness regions, and highways. Therefore, animal detection is a vital safety and wildlife conservation component that can mitigate the negative impacts of these encounters. Deep learning techniques have achieved the best results compared to other object detection techniques; however, they require many computations and parameters. A lightweight animal species detection model based on YOLOv2 was proposed. It was designed as a proof of concept of and as a first step to build a real-time mitigation system with embedded devices. Multi-level features merging is employed by adding a new pass-through layer to improve the feature extraction ability and accuracy of YOLOv2. Moreover, the two repeated $3\times $ 3 convolutional layers in the seventh block of the YOLOv2 architecture are removed to reduce computational complexity, and thus increase detection speed without reducing accuracy. Animal species detection methods based on regular Convolutional Neural Networks (CNNs) have been widely applied; however, these methods are difficult to adapt to geometric variations of animals in images. Thus, a modified YOLOv2 with the addition of deformable convolutional layers (DCLs) was proposed to resolve this issue. Our experimental results show that the proposed model outperforms the original YOLOv2 by 5.0% in accuracy and 12.0% in speed. Furthermore, our analysis shows that the modified YOLOv2 model is more suitable for deployment than YOLOv3 and YOLOv4 on embedded devices.

Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.

Advances in deep learning have led to the development of various animal species detection models suited for different environments. Building on this, our research introduces a detection model that efficiently handles both batch and real-time processing. It achieves this by integrating a motion-based frame selection algorithm and a two-stage pipelining–dataflow hybrid parallel processing approach. These modifications significantly reduced the processing delay and power consumption of the proposed MCFP-YOLO detector, particularly on embedded systems with limited resources, without trading off the accuracy of our animal species detection system. For field applications, the proposed MCFP-YOLO model was deployed and tested on two embedded devices: the RP4B and the Jetson Nano. While the Jetson Nano provided faster processing, the RP4B was selected due to its lower power consumption and a balanced cost–performance ratio, making it particularly suitable for extended use in remote areas.