Object Detection in Satellite Images with Canis Hunt Optimized Tetralet Attention enabled Explainable Convolutional Neural Network
Keywords:
Object detection, Satellite images, Explainable Convolutional Neural Network, Tetralet Attention, and Canis Hunt OptimizationAbstract
Object detection has always been a research hotspot in computer vision, specifically detection from satellite images remains a challenging research area. Several conventional researches have been developed but failed to work with high-quality images and satellite images. The object detection in the research is proposed with the Canis Hunt Optimized Tetralet Attention enabled Explainable Convolutional Neural Network (CHunt-TetraExNN). The proposed model aims to provide accurate detection from the satellite image inputs. The Tetralet attention module incorporated with the model is composed of triplet attention followed by positional attention, which provides the accurate estimation of the attentional features that are highly helpful in the process of object detection. Further, the research model is supported by the Canis Hunt Optimization, which is the combination of the adaptability and the hunt characteristics of the Lapins and Latrans that belong to the Canis Family. Thus, the model provided accurate estimation outcomes in the research of object detection from the satellite images, which are estimated with an Accuracy of 96.55%, Precision of 94.59%, Recall of 96.56%, and F1 score of 95.6%.
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