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This study aims to solve the problems of inaccurate weighing of bamboo sticks and inefficient manual counting. To overcome this problem, an improved MobileNetV3 model and a counting algorithm suitable for bamboo sticks&#x2014;combined with a spatial-temporal attention mechanism&#x2014;are proposed in this paper. Inspired by the idea of EfficientNet, scaling coefficients are used to scale the MobileNetV3 network structure as a whole in terms of width and height. The optimal model for bamboo-stick recognition is screened initially, and then the algorithm uses the convolutional block attention module (CBAM) attention mechanism to replace the squeeze-and-excitation (SE) attention mechanism in the MobileNetV3 network structure to allow the network to extract features in the two dimensions of channel and space. Since the number of bamboo sticks in a single image is extremely dense&#x2014;generally around 1000&#x2013;3000&#x2014;it is difficult to effectively count them with existing algorithms. The proposed algorithm divides the image into multiple equally sized blocks and then uses the boundary processing algorithm to merge the cut bamboo stick images and count the number of sticks. Experimental results show that the proposed algorithm can effectively perform near real-time detection on a mobile terminal and its accuracy can reach approximately 97&#x0025;, which is in line with actual production applications.

MobileNetV3 With CBAM for Bamboo Stick Counting