Precise Segmentation of Remote Sensing Cage Images Based on SegNet and Voting Mechanism

Highlights A Remote Sensing Cage Segmentation (RSCS) dataset is constructed. The SegNet network is introduced to achieve precise segmentation. Random cropping, data augmentation, and three-channel separation operations are used to construct the training sets. The proposed sliding window overlap cropping method and two rounds of voting are used to improve the segmentation accuracy. Abstract. In mariculture, improper cage layout and excessive density of mariculture will lead to deterioration of water quality and the growth of harmful bacteria. However, relying solely on manual measurement will consume a considerable amount of manpower and material resources. Therefore, we propose a precise segmentation scheme for remote sensing cage images based on SegNet and voting mechanism. First, a Remote Sensing Cage Segmentation (RSCS) dataset is constructed. Second, the number of collected samples is too small and the sample sizes are too large. Random cropping, data augmentation, and three-channel separation operations are used to construct the training sets. Nine training sets consisting of three image sizes and three single channels are generated. Finally, the proposed sliding window overlap cropping method and two rounds of voting are used on the test samples to improve the segmentation accuracy. The experimental results show that using sliding window overlap cropping, three-channel voting, and three-size voting can improve mIoU (mean Intersection over Union) by up to 0.9%, 1.9%, and 0.6%, respectively. By using the proposed final scheme, the mIoU of test samples can reach 0.89. Keywords: Mariculture, Remote image segmentation, SegNet, Sliding window overlap cropping, Voting mechanism.