Aiming at the problem of limited recognition accuracy caused by the loss of shallow features in the detection of micro cracks on plate heat exchanger surfaces, this paper proposes a detection method based on a lightweight dense convolutional network (DenseNet) architecture. As a single-category, multi-scale distributed cluster of linear small objects, the core challenge of microcrack detection lies in the effective learning and preservation of spatial detail features. The main contributions are as follows: First, a theoretical model matching the receptive field to defect size is established, with mathematical formulas derived for hierarchical configuration, followed by model instantiation based on the characteristics of corrugated plate defects in real industrial scenarios. Second, a detail enhancement mechanism is designed, which preserves critical spatial features by disabling downsampling operations and progressively expands the receptive field through stacked 3×3 small convolutional kernels, effectively balancing feature resolution and semantic abstraction. Finally, a defect instancelevel evaluation strategy is constructed to meet the national standard requirements, which focus on the "existence detection" of microcracks rather than size measurement. Experimental results on the Shenyang University of Technology Benchmark 1(SUT-B1) dataset show that the proposed method achieves an average precision of 94.69% and an F1-score of 92.60%, with only 3 missed detections and 5 false detections. Its performance not only surpasses that of baseline and mainstream lightweight models, which achieve namely, 94.53% AP and 87.85% F1-score—demonstrating the advantage of the DenseNet structure in feature reuse, but also exceeds the best performance in comparative experiments (94.56% AP and 90.90% F1-score), confirming both the advantage of DenseNet-based feature reuse and the necessity of the structural optimization strategy. The proposed approach demonstrates practicality and scalability in the field of industrial inspection, offering a new technical direction for similar fine defect recognition tasks. The related code is publicly available at: https://github.com/zhuanzhaun/Lightweight-DenseNet.