Abstract
In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.
Highlights
As a major maritime country, China covers a sea area of up to 4.73 million m2, which comprises 280 million ha of shelf fishing grounds, 2.6 million ha of shallow aquaculture ponds, 17.47 million ha of inland waters, and 67 million ha of untapped resources suitable for fisheries such as salinealkaline lands [1]
According to the detection results, the Faster R-convolutional neural networks (CNNs) with ResNet exhibits an over 77% mAP in various shellfish detections
A deep learning algorithm for shellfish identification is proposed in order to address the inefficiency of conventional detection algorithms under different ambient light, diverse backgrounds, and varying occlusion conditions
Summary
As a major maritime country, China covers a sea area of up to 4.73 million m2, which comprises 280 million ha of shelf fishing grounds, 2.6 million ha of shallow aquaculture ponds, 17.47 million ha of inland waters, and 67 million ha of untapped resources suitable for fisheries such as salinealkaline lands [1]. Us, it is imperative to increase investment in fisheries in order to strengthen the development and utilization of waters and to improve the comprehensive production capacity of the aquaculture industry. Regarding the sorting process in shellfish production, substantial manual input is required, which restricts the large-scale development of the shellfish industry severely. By using computer vision for the identification and localization of scallops and subsequent sorting, the production efficiency can be improved while ensuring the quality of aquatic products. In the meantime, it reduces the demand for labor
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