Enhanced SOLOv2: An Effective Instance Segmentation Algorithm for Densely Overlapping Silkworms

Silkworm instance segmentation is crucial for individual silkworm behavior analysis and health monitoring in intelligent sericulture, as the segmentation accuracy directly influences the reliability of subsequent biological parameter estimation. In real farming environments, silkworms often exhibit high density and severe mutual occlusion, posing significant challenges for traditional instance segmentation algorithms. To address these issues, this paper proposes an enhanced SOLOv2 algorithm. Specifically, (1) in the backbone network, Linear Deformable Convolution (LDC) is incorporated to strengthen the geometric feature modeling of curved silkworms. A Haar Wavelet Downsampling (HWD) module is designed to better preserve details for partial visible targets, and an Edge-Augmented Multi-attention Fusion Network (EAMF-Net) is constructed to improve boundary discrimination in overlapping regions. (2) In the mask branch, Dynamic Upsampling (Dysample), Adaptive Spatial Feature Fusion (ASFF), and Simple Attention Module (SimAM) are integrated to refine the quality of segmentation masks. Experiments conducted on a self-built high-density silkworm dataset demonstrate that the proposed method achieves an Average Precision (AP) of 85.1%, with significant improvements over the baseline model in small- (APs: +10.2%), medium- (APm: +4.0%), and large-target (APl: +2.0%) segmentation accuracy. This effectively advances precision in dense silkworm segmentation scenarios.