Abstract Traditional methods of breeding soft-shell crabs mainly rely on manual identification, which has high costs regarding manpower and resources. Manual inspection may also interfere with crabs’ molting, causing molting failure, and possibly even death, which is costly and inefficient. This paper combines an improved YOLOv3 algorithm with an adaptive dark-channel defogging algorithm to realize the real-time detection of whether a swimming crab in a single-crab basket-culture system is molting. For learning more features, affine, rotation transformation and local occlusion are used to augment the training data to simulate the difficulty of identification caused by occlusion, in case molting may occur under distorted viewing conditions in real culture environments. A k-means++ clustering algorithm is used to obtain prior boxes matching the size of the carapace throughout the entire breeding cycle, and so improve the Intersection over Union (IOU). The identification network itself can have its network structure pruned and the non-maximum suppression function modified to increase rapidity and accuracy; the improved network can recognize and give early warning of the early stage of molting. The precision of the improved model in clean water reaches 100 %, and the running speed was 31 FPS. In turbid water where the prediction confidence is lower than the cut-in threshold of defogging algorithm set as 0.8, the precision of the improved model was over 91 %, and the speed can still maintain about 7 FPS.

中文翻译：

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一种改进的YOLOv3算法在复杂背景下检测游蟹蜕皮

摘要 传统的软壳蟹养殖方法主要依靠人工鉴定，人力物力成本高。人工检查也可能会干扰螃蟹的蜕皮，导致蜕皮失败，甚至可能导致死亡，成本高昂且效率低下。本文将改进的YOLOv3算法与自适应暗通道去雾算法相结合，实现了单蟹篮培养系统中的大闸蟹是否正在蜕皮的实时检测。为了学习更多的特征，使用仿射、旋转变换和局部遮挡来增加训练数据，以模拟遮挡造成的识别困难，以防在真实文化环境中在扭曲的观看条件下可能发生蜕皮。使用 k-means++ 聚类算法在整个繁殖周期中获得与甲壳大小匹配的先验框，从而改进联合交叉（IOU）。识别网络本身可以修剪网络结构，修改非极大值抑制函数，以提高快速性和准确性；改进后的网络可以识别蜕皮早期并给出预警。改进后的模型在清水中的精度达到100%，运行速度为31 FPS。在预测置信度低于除雾算法切入阈值0.8的混浊水中，改进模型的精度超过91%，速度仍能保持7FPS左右。识别网络本身可以修剪网络结构，修改非极大值抑制函数，以提高快速性和准确性；改进后的网络可以识别蜕皮早期并给出预警。改进后的模型在清水中的精度达到100%，运行速度为31 FPS。在预测置信度低于除雾算法切入阈值0.8的混浊水中，改进模型的精度超过91%，速度仍能保持7FPS左右。识别网络本身可以修剪网络结构，修改非极大值抑制函数，以提高快速性和准确性；改进后的网络可以识别蜕皮早期并给出预警。改进后的模型在清水中的精度达到100%，运行速度为31 FPS。在预测置信度低于除雾算法切入阈值0.8的混浊水中，改进模型的精度超过91%，速度仍能保持7FPS左右。改进后的模型在清水中的精度达到100%，运行速度为31 FPS。在预测置信度低于除雾算法切入阈值0.8的混浊水中，改进模型的精度超过91%，速度仍能保持7FPS左右。改进后的模型在清水中的精度达到100%，运行速度为31 FPS。在预测置信度低于除雾算法切入阈值0.8的混浊水中，改进模型的精度超过91%，速度仍能保持7FPS左右。