Partial-Channel Mamba for Surface Defect Detection on Cable-like Structures: Channel Allocation and Backend-Aware TensorRT Evaluation

Slender cable-like surface defects often exhibit weak texture and indistinct boundaries, requiring both local detail and long-range context. We introduce YOLOv8s-PMNL, which inserts Partial Mamba Neck Lite (PMNL) at two YOLOv8s neck fusion sites while retaining the backbone and detection head. PMNL allocates projected channels between local convolution and selective state-space modeling. We evaluate five channel-allocation ratios, parameter-matched controls, held-out testing, and retraining on a second public dataset. On a wire-rope dataset, three-seed validation gives (70.191±0.215)% mean average precision (mAP) at an intersection-over-union (IoU) of 0.50:0.95, 4.566 percentage points above YOLOv8s; the held-out test mAP is 69.636%. PMNL improves over parameter-matched convolution by 2.515 percentage points, whereas Full Mamba retains higher aggregate accuracy. Under matched TensorRT 32-bit floating-point (FP32) core inference on an RTX 4090, PMNL reduces latency and device-memory requirement relative to Full Mamba by 9.32% and 16.88%, respectively. A separate 1114-image software-path end-to-end benchmark measures 8.708±0.161 ms for PMNL and 8.878±0.015 ms for Full Mamba, corresponding to a 1.920% lower mean latency and 1.958% higher throughput under that protocol. Together, these measurements define a backend- and timing-scope-dependent accuracy–efficiency trade-off; they do not establish universal acceleration.