Real-time robust detector for underwater live crabs based on deep learning

Abstract

Image analysis technology has drawn dramatic attention and developed rapidly because it enables a non-extractive and non-destructive approach to data acquisition of crab aquaculture. Owing to the irregular shape, multi-scale posture and special underwater environment, it is very challenging to adopt the traditional image recognition methods to detect crabs quickly and effectively. Consequently, we propose a real-time and robust object detector, Faster MSSDLite, for detecting underwater live crabs. Lightweight MobileNetV2 is selected as the backbone of a single shot multi-box detector (SSD), and standard convolution is replaced by depthwise separable convolution in the prediction layers. A feature pyramid network (FPN) is adopted at low extra cost to improve the detection precision of multi-scale crabs and make up for the deficiency of SSD to force different network layers to learn the same features. More significantly, the unified quantized convolutional neural network (Quantized-CNN) framework is applied to quantify the error correction of the improved detector for further accelerating the computation of convolutional layers and compressing the parameters of fully-connected layers. The test results show that Faster MSSDLite has better performance than traditional SSD. The average precision (AP) and F1 score of detection are 99.01% and 98.94%, respectively. The detection speed can reach 74.07 frames per second in commonly configured microcomputers (~8× faster than SSD). The computation amount of floating-point numbers required by the detection is reduced to only 0.32 billion (~49× smaller than SSD), and the size of the model is compressed into 4.84 MB (~28× smaller than SSD). The model is also more robust, which can stably detect underwater live crabs in real-time, estimate the live crab biomass in water bodies automatically, and provide reliable feedback information for the fine feeding of automatic feeding boats.