DMDnet: A decoupled multi-scale discriminant model for cross-domain fish detection

Object detection technology is significant for automatic fish monitoring and intelligent aquaculture. Deep learning fish detection provides great convenience for aquaculture with its unique advantages. However, it still faces several challenges in practical applications. For example, due to the perspective projection effect, the different scales of non-rigid fish will cause difficulties in the identification and location. Also, when labelled data collection is expensive or not feasible, domain shifts due to differences in data distribution can severely limit the practical deployment of algorithms in aquaculture. Hence, this paper proposes a decoupled multi-scale discriminant model especially for cross-domain fish detection to solve these problems, termed DMDnet. The model is divided into three components: multi-scale feature enhancement detector, category adaptive module, and adaptive regression module. An enhanced feature pyramid is embedded into the detector to alleviate the multi-scale problem of fish and improve the discrimination of the whole model. Category and regression adaptors with independent parameters are also introduced to avoid the damage of adversarial training on the detector's discriminability. These two adaptors are separated from the detector, to improve transferability. The cross-domain experiments were carried out on underwater fish images collected from various scenes and aquaculture conditions. The results verify that this method can improve the detector's generalisation performance significantly in the unlabelled new domain. Hence, this method can reduce the cost and increase the efficiency of aquaculture, which has a better application prospect. (c) 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.