<p>Locusts are a notorious pest that severely impacts agricultural production in China. Efficient segmentation and detection of camouflaged locusts are essential. However, previous segmentation methods often exhibit limitations in handling complex backgrounds or struggles with fine detail processing, particularly with small features like locust antennae. To address this problem, we propose an EW-CASCADE model and introduce a new dataset, Camouflaged Locust. We design an Enhanced Efficient Multi-scale Attention (EEMA) module that leverages parallel sub-networks and spatial learning to avoid dimensionality reduction, thereby preserving additional feature information while enhancing the model’s ability to capture long-range dependencies. Meanwhile, we introduce Wavelet Transform Convolution (WTConv) and incorporate it to decompose information effectively across multiple scales, thus improving the decoder’s global perception as well as the edge and texture details. Experimental results demonstrate that the proposed EW-CASCADE model achieves superior segmentation accuracy on the Camouflaged Locust dataset compared to the previous state-of-the-art approaches, particularly in capturing fine details and low-contrast regions. Our model improves by 0.057 in mDic, 0.079 in mIoU, 0.071 in wFm, and 0.040 in Sm, while the MAE decreases from 0.022 to 0.013.</p>