High-resolution synthetic aperture radar (SAR) ship detection plays a pivotal role in maritime surveillance and ocean monitoring. However, it remains challenging in practice because of the single-channel imaging modality, severe multiplicative speckle noise, and the pronounced scale imbalance in which sparse large vessels are easily under-optimized compared to the dominant small and medium instances. In this paper, we propose a Texture Perception Convolution Network (TPCNet), a practical and reproducible detection framework to improve feature extraction robustness and high-IoU localization under a unified strict-COCO evaluation protocol. TPCNet begins with a lightweight texture perception convolution (TPConv) that augments the raw SAR intensity with a local fluctuation cue to stabilize early feature representations for SAR images affected by strong multiplicative speckle noise (speckle-rich imagery). To address scale skew during training without modifying dataset splits, a region-balanced sampler (RBS) is introduced to increase the sampling probability of images, thereby improving the effective exposure of informative large-target structures. A background similarity augmentation (BSA) is proposed to enrich medium and large instances while reducing unrealistic boundary artifacts via compatible background selection and soft blending. Beyond component-level designs, the high-IoU localization is highly sensitive to geometric perturbations. Accordingly, TPCNet adopts a two-stage localization-oriented training strategy that first learns robust multi-scale representations and then refines box regression by tightening translation and scale ranges during fine-tuning. Under strict-COCO settings, TPCNet achieves SOTA performance with an AP of 71.20% on HRSID and an AP of 72.7% on SSDD. Comprehensive ablation studies demonstrate that TPConv, RBS, BSA, and the proposed finetuning strategy contribute complementary gains, providing a transparent baseline and a strong recipe for future SAR ship detection research.