At present, the collection of floating garbage such as dead fish in deep and far sea cage culture waters mainly depends on manpower, which is not only difficult and inefficient, but also has great potential safety hazards. In order to solve the above problems, a water surface collection robot based on marine cage culture is designed and developed. The dead fish on the water surface are collected through the joint action of propeller and forearm drainage and retraction device. At the same time, the relevant performance indexes of the designed water surface collection robot are verified by making the prototype and water surface collection experiments, which meets the needs of simple and efficient collection and cleaning of dead fish in deep and open sea cage culture, so as to provide a reference basis for the practical application of the collection robot.

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Pearl detection with a counter (PDC) in a noncontact and high-precision manner is a challenging task in the area of commercial production. Additionally, sea pearls are considered to be quite valuable, so the traditional manual counting methods are not satisfactory, as touching may cause damage to the pearls. In this paper, we conduct a comprehensive study on nine object-detection models, and the key metrics of these models are evaluated. The results indicate that using Faster R-CNN with ResNet152, which was pretrained on the pearl dataset, mAP@0.5IoU = 100% and mAP@0.75IoU = 98.83% are achieved for pearl recognition, requiring only 15.8 ms inference time with a counter after the first loading of the model. Finally, the superiority of the proposed algorithm of Faster R-CNN ResNet152 with a counter is verified through a comparison with eight other sophisticated object detectors with a counter. The experimental results on the self-made pearl image dataset show that the total loss decreased to 0.00044. Meanwhile, the classification loss and the localization loss of the model gradually decreased to less than 0.00019 and 0.00031, respectively. The robust performance of the proposed method across the pearl dataset indicates that Faster R-CNN ResNet152 with a counter is promising for natural light or artificial light peal detection and accurate counting.

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