YOLO-Pooling: Exploring the Potential of Pooling Operations in Object Detection

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Pooling operations play a crucial role in object detection by reducing feature map dimensions, enhancing position invariance, enabling multi-scale detection capabilities, and lowering computation overhead, which is particularly important in industrial inspection scenarios such as metal surface defect detection, concrete crack detection, hot-rolled strip inspection, and PCB defect analysis where complex backgrounds and subtle defects demand high accuracy and robustness. However, existing architectures (e.g., FPN/PANet, U-Net with attention) rely on single-type pooling or simple multi-scale concatenation, leading to information loss or inefficient feature aggregation. To address these limitations, we aim to explore the potential of pooling operations in the context of object detection tasks. To this end, we propose two modules based on pooling operations. The first is the dimension reduction pooling (DRP) module, which differs from traditional single pooling by combining 3×3 max pooling and average pooling during dimension reduction (to preserve both salient features and global distribution) and compressing channels via 1×1 convolution (to avoid redundancy), providing a richer feature representation. The second is the multi-scale feature aggregation (MFA) module, which innovatively integrates CSP structure, multi-step weighted feature aggregation, and stereoscopic attention (parallel channel-spatial attention)—distinct from U-Net’s symmetric mapping and FPN/PANet’s simple pathway concatenation. It seamlessly integrates coarse semantic information with fine-grained semantic information through top-down and bottom-up pathways, employs a stereoscopic attention mechanism to enhance feature representation, expands perception scope, and improves generalization capabilities. Based on these two modules, the YOLO-Pooling is proposed, an object detection model that progressively refines deep and shallow semantic features. The proposed method is evaluated on six public datasets: GC10-DET, Crack, Barcodes, NEU-DET, PCB, and a subset of COCO, and the mAP of the method is 67.74%, 86.11%, 97.78%, 73.27%, 96.56%, and 6.49%, respectively, significantly higher than the state-of-the-art detection methods. Experimental results demonstrate that the proposed DRP + MFA design outperforms existing multi-scale aggregation and pooling-based architectures by solving the trade-off between pooling-induced information loss and model accuracy, fundamentally improving object localization and detection accuracy, and maintains efficient inference speed. This efficiency stems from precise FLOPs control, optimized memory access patterns, and operator fusion, enabling higher fps than many mainstream YOLO models despite additional modules (DRP and MFA).