# Brief Review — TPH-YOLOv5++: Boosting Object Detection on Drone-Captured Scenarios with Cross-Layer Asymmetric Transformer

## TPH-YOLOv5++, Improves TPH-YOLOv5

TPH-YOLOv5++: Boosting Object Detection on Drone-Captured Scenarios with Cross-Layer Asymmetric Transformer,TPH-YOLOv5++, by Beihang University,2022 MDPI J. Remote Sensing(Sik-Ho Tsang @ Medium)

Object Detection2014 … 2021[Scaled-YOLOv4] [PVT, PVTv1] [Deformable DETR] [HRNetV2, HRNetV2p] [MDETR] [TPH-YOLOv5]2022[PVTv2] [Pix2Seq] [MViTv2] [SF-YOLOv5] [GLIP]2023[YOLOv7]

==== My Other Paper Readings Are Also Over Here ====

**TPH-YOLOv5++**is proposed to**significantly reduce the computational cost****and improve the detection speed of****TPH-YOLOv5****.**- In TPH-YOLOv5++,
**cross-layer asymmetric****Transformer****(CA-Trans)**is designed to**replace the additional prediction head**while maintain the knowledge of this head. - By using a
**sparse local attention (SLA)**module, the**asymmetric information between the additional head and other heads**can be captured efficiently, enriching the features of other heads.

# Outline

**Problems of****TPH-YOLOv5****TPH-YOLOv5++****Results**

**1. Problems of **TPH-YOLOv5

## 1.1. TPH-YOLOv5

- TPH-YOLOv5 introduces
**an additional head,****Transformer****prediction head (TPH)**, and**convolutional block attention module (CBAM)**. **Four prediction heads**are named**tiny**,**small**,**medium**, and**large heads**.

## 1.2. Problems at Tiny Head

At top left of the figure, the confidences of all boxes at

tiny headare shown. It producesplenty of wrong boxes with relatively large confidence, especiallybetween 0.2 and 0.6.

- The average confidence value of each pixel is estimated. If the
**average confidence**is**high**, the color tends towards**red**, otherwise it tends towards blue. - It is found that
**the small prediction head also captures objects of considerable proportions that are contained by the results predicted by the additional tiny head.**

**2. TPH-YOLOv5++**

## 2.1. Pipeline

- Instead of
**small path and tiny path**for predicting objects individually, these two paths are**fused together**via proposed**CA-Trans**:

- where CA-Trans, taking
**f1**and**f’2**as**inputs**and getting**f’’2**.**CA — Trans( , )**denotes the**cross-layer asymmetric****Transformer**

## 2.2. **CA-Trans**

CA-Trans takes

asf1 andf’2inputsandgenerates.f’’2

Then,

fromKandQare generated:f1

- where LN is Layer Norm.

Similar to

KandV,Qis generated fromf’2:

## 2.3. Sparse Local Attention (SLA)

- After obtaining
*Q*,*K*, and*V*, we use the**sparse local attention (SLA)**module to**calculate sparse attentions between features of two different layers**. - First,
**neighborhood area of**is obtained. Therefore,*Kp*for each pixel in*Q***a neighborhood set for**is obtained:*Q*

- where
is*M***the set of neighborhood features of all pixels in**, and*Q*contains the*Mij***(**.*i*,*j*) neighborhood feature of each pixel in*Q* - The transformation of each pixel from
*Kp*to*Mij*:

- where
*u*and*v*are the coordinates of pixel in*Mij*. **All the neighborhood features**are**concatenated**as:*Ksparse*

- The
**sparse relation map**is then calculated:*R*

- where
*dQ*is the channel dimension of*Q*. - Then
**asymmetric feature extractor (AFE)**is used to obtain the asymmetric map:

- where
**the value at each location of**is:*A*

- where
*Ri*denotes the average of the i-th row of*R*. - The
**asymmetric map is applied to**to extract the features:*V*

- where
denotes*Fa***the asymmetric feature output from the SLA and softmax(.)**is the**softmax**operation along each row of*A*. - Finally,
**the output**can be formulated as:*Fout*of CA-Trans

# 3. Results

## 3.1. SOTA Comparisons

Compared to TPH-YOLOv5,

TPH-YOLOv5++ is about 1% lower on three metricsbutstill gets higher AP and AP75 than other methods.

**ViT-YOLO [61]**, which also wins**2nd place in the VisDrone Challenge 2021**, achieves the SOTA results. (Hope I can read about ViT-YOLO later.)

Compared to other methods,

TPH-YOLOv5andTPH-YOLOv5++havecomparable results.

- TPH-YOLOv5 obtains more than a 2% performance gain at least, and is even 4.7% higher than UFPMPNet [49] on AP75.

Based onTPH-YOLOv5, TPH-YOLOv5++ further improves the performanceto 30.1%, 43.5%, and 34.3% on these three metrics, which mean 3.2%, 2.2%, and 1.5% gains.For

datasets with less high-density sceneslike UAVDT,TPH-YOLOv5++ can significantly overcome the shortcomings ofTPH-YOLOv5.

## 3.2. Visualizations

- For example, on the
**first row**, the**TPH-YOLOv5**detects the**bus**as**multiple objects**, while**TPH-YOLOv5**can**significantly decrease these wrong bounding boxes.** - On the
**second row**,**TPH-YOLOv5****a bounding box across the top two cars**. By contrast,**TPH-YOLOv5++ correctly predicts two cars.** - (For ablation studies, please feel free to read about the paper directly.)