Enhanced Animal Detection in Complex Outdoor Environments Using Modified Yolo V7
Keywords:
Anima detection, YOLO v7, Challenging image conditions, R-CNNAbstract
Detecting animals accurately and quickly in complicated outdoor settings is important for getting work done efficiently. But it's not easy because the places where animals live have complicated environmental conditions. This research proposes a novel animal detection method that uses the YOLO V7 network to overcome these challenges. Thorough evaluations and comparisons are performed on various detection networks like YOLO V3-spp, YOLO V5s, Faster R-CNN, and YOLO V7, which are meticulously conducted. The rigorous assessments identify YOLO V7 as the preeminent performer. The findings are noteworthy, as the model exhibits exemplary detection capabilities and robust adaptability in complex field environments. It attains a noteworthy mean Average Precision (mAP) of 96.03%, accompanied by impressive precision, recall, F1 score, and an average detection time of 94.76%, 95.54%, 95.15%, and 0.025 seconds per image, respectively. This study underscores the profound efficacy of uniting YOLO V7 for animal detection within challenging field conditions.
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