Abstract
Among the most critical problems is minimising vehicle-animal accidents on highways, which cause environmental imbalances and huge public expenditures. This work covers the components of a detect and categorises the species of trapped picture with crop using bbox detect by automatically selecting the shared closest neighbour pixel to detect the large data set detected by MegaDetector. The model automatically selects the weighted average pixel using KNN regression to find the nearest neighbour of SNN density to group the minimal number of points. The ASNNP model crop the trapped image with high accuracy as well as, minimal loss identified in learning rate. The proposed and presented techniques are evaluated based on their ability to meet the mean average precision 8.02 MB (mAP) and detection speed with 94.2 in VGG16.
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Anantha Babu, S., Manikandan, V., Jaiganesh, M., John Basha, M., Divya, P. (2023). Detecting Wildlife Trapped Images Using Automatically Shared Nearest Neighbouring Pixels (ASNNP). In: Smys, S., Kamel, K.A., Palanisamy, R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-19-7402-1_1
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