Addressing the key issues in coal mine underground environments, such as poor imaging quality under low illumination, high miss-detection rates for small-scale foreign objects, and feature information loss caused by object occlusion, this paper proposes a low-illumination lightweight foreign object detection model, DEL-YOLO, based on YOLOv11s. Firstly, in the image preprocessing stage, the Contrast-Limited Adaptive Histogram Equalization algorithm is introduced to enhance the detailed features of low-illumination images, effectively improving the visibility of foreign objects in dark areas. Secondly, at the network architecture level, an innovative feature extraction module, DE-Block, is designed, and a DE-C3K2 module is constructed to extract features from foreign objects with irregular shapes and overlapping occlusions. Furthermore, a feature fusion module, EFC, is embedded in the neck network, which suppresses redundant feature fusion through an interlayer feature correlation enhancement mechanism while strengthening the feature representation capability for small objects. Finally, a lightweight detection head, L-Detect, is designed, which achieves parameter compression through a neck feature sharing strategy. Experimental results show that DEL-YOLO achieves an average detection accuracy of 80.8%. Compared with YOLOv11, it improves the average precision rate by 4.9%, reduces the computational load by 40.74%, and decreases the number of parameters by 41.75%, with a model size of only 6.45 MB. While significantly reducing complexity, the improved model can still effectively address the issues of small object miss-detection and occluded object detection in the complex low-illumination environments of coal mines.