An approach to ship target detection based on combined optimization model of dehazing and detection

Abstract

The design of a ship detection model that can be adapted to both foggy and clear images faces significant challenges. Existing methods are either not accurate enough, or have a high amount of model parameters, making them difficult to deploy to lightweight front-ends. To address these issues, a lightweight deep learning model based on combined optimization of dehazing and detection is proposed, focusing on self-adaptive ship detection. Firstly, a self-adaptive image dehazing module is designed and placed ahead of the detection network, including a dehazing parameter predictor and an improved dehazing method. Subsequently, a lightweight-improved object detection deep learning model integrated with the dehazing module is devised to detect the ship in the foggy image. Experimental results demonstrate the effectiveness of this approach in enabling efficient and accurate ship detection under foggy conditions. Through the joint optimization of the dehazing module and the detection module, it can be seen from the experiments that our Dehazing + Detection model has the highest detection accuracy and performs well in terms of detection speed, parameter amount, and weight file size. The detection accuracy has reached 97.1%, which is better than that of the other three dehazing + detection models.