This has lead the way for responsible actions and creative strategies in a number of disciplines relating to wildlife, including species discovery, habitat monitoring, and conservation initiatives. This study mainly focuses on the classification and detection of lions using the YOLOv5 algorithm and ...
By leveraging advanced computer vision techniques, machine learning algorithms, and large-scale datasets, we strive to create a reliable solution that can assist in wildlife conservation efforts, animal monitoring, and research initiatives. The primary objective of this project is to utilize state-of-...
Explore state-of-the-art image classification models from YOLOv5 to OpenAI CLIP and learn about their main features on Roboflow Models.
This has lead the way for responsible actions and creative strategies in a number of disciplines relating to wildlife, including species discovery, habitat monitoring, and conservation initiatives. This study mainly focuses on the classification and detection of lions using the YOLOv5 algorithm and ...
determined by the researcher, the prediction was deemed to be a true positive. The IoU threshold value was determined as 0.5 in this study. Finally, the mAP value was calculated using formula (1), whereQis the number of queries of the dataset, and AP(q) is the AP for the given query...
To establish the benchmark dataset, we evaluated the dataset using several DL models, including YOLOv7, YOLOv8 and Faster-RCNN, to locate and classify weeds in crops. The performance of the models was compared based on inference time and detection accuracy. YOLOv7 and its variant YOLOv7-...
The classification of individual features of wood microscopic images by manual annotation is typically performed using an object-detection model that contains backbone networks, neck networks, detection heads or other components, such as YOLO (You Only Look Once) [26], SSD (Single Shot MultiBox Det...
Label-efficient learning (Bothmann et al., 2023) Wildlife image classification 82.66% Transformer-based network utilizing CNN (Dwivedi et al., 2024) Environmental microorganisms classification 71.17% BleafNet (Pre-trained ResNet- 50) (Ganguly et al., 2022b) Plant leaf classification 98.7% DDYOLO...
Twenty-two incidents are identified in which 67 volcanologists, other field scientists and those supporting their work died (Table7). This includes volcano observers, field assistants, ship’s crew, geology students (on fieldwork), and a U.S. Fish and Wildlife Service volunteer. The latter two...
To enable the automatic collection of data for the experiment, a simulated environment was created to simulate four classes of wildlife found in South Africa: buffalo, elephants, rhino and zebra. The network structure for the detector network selected was an adapted version of the tiny YOLOv3 ...