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The present application relates to the field of computer vision, and specifically discloses a DHA algal oil soft capsule filling amount insufficient detection method based on a deep learning neural network, comprising: accurately separating each capsule instance from a production line transmission image through a lightweight instance segmentation model; synchronously analyzing the global contour and local density features of a single capsule region using a multi-scale feature extraction network, which innovatively introduces an auxiliary density estimation module to enhance the perception of the content filling state; and fusing the above features to complete the accurate classification of the filling state. The present application effectively solves the practical problems of feature confusion, insensitivity to minor changes, poor dense detection effect, and the difficulty in balancing model efficiency and accuracy, and realizes high-precision and high-efficiency automatic detection of DHA algal oil soft capsule filling amount insufficient.